Case Studies

JMI Implementation

• Conducted detailed research on company and valued the company using SOTP method and company comparable.
• Valued 37% of investor’s equity value at $65M and also identified the realized gain of $61M during 19-year holding period.
• Assessed that the company has one of its best investment in portfolio with MoIC of 32.6x and IRR of 39.2%.
• Based on analysis done, JMI team identified that the size of market remains a constraint for further growth potential and that company had reached its maturity in their strategic frames.
• JMI recommended two options:

JMI Implementation

• Began the search by classifying 10,000+ companies.
• Acquired the data list from multiple sources such as Bloomberg, industry associations, and trade journals.
• Filtered and cleaned the list down to 500 companies based on size and profitability.
• As per client’s request, focused on 5 industry segments to understand the dynamics.
• Profiled 66 companies with a special focus on management and their affiliations, company financials, operations, and valuation ratios.

JMI Implementation

• Performed screening for public and private entities in the renewable energy space and compiled a list based on their offerings.
• Filtered and reviewed renewable energy companies with a detailed analysis and history of all private equity investments and acquisitions in the sector.
• Computed their current and forward trading multiples and arrived at a range of exit multiples.
• Provided the range of valuation multiples based on the analysis and helped identify KPI enabling premium valuation.

Solution

JMI’s solution comprised of the following approach:

• Operational level data: Extracted operational level data such as sales statistics, inventory levels, ordering/purchasing data, working capital metrics, customer acquisition costs, and other KPIs for high-level scrutiny of portfolio companies.
• Mapping with the current trends: Operational level data of portfolio companies was dissected further and benchmarked with the latest trends and developments to gain deeper operational insights.
• Creating value: This lead to the identification of a specific focus areas for each portfolio company along with data insights on revenue and costs. Insights across the portfolio companies further assisted the PE firm to implement best practice of one portfolio company to all others leading to improvement in the overall portfolio.

JMI Implementation

• Extracted the historical data and analyzed various data like shipment order type, from - to details etc. to design daily warehouse stock and inventory reporting tool for warehouse managers. This gives them a comprehensive overview of the level and location of stock, and shows recommendations on where to store each item based on customer demand.
• Analytics team also extracted three years of historical shipping data to predict the number of outbound and inbound shipping orders per warehouse over daily, weekly and monthly horizons. It helped the company to manage their labor planning and maintain the right level of staffing to deal with customer demand on any given day.
• Observed that during the during summer holidays in US, there was a spike in demand for automobile parts and which required a huge workforce for better supply chain management.
• Built an optimization model for their client which helped them to solve complex distribution network problems and provided optimal solutions within a given set of parameters. JMI team also observed that 8 of its distribution center had lower sales compared to average sales figures because of longer deliverable time.
• Provided insights to clients for better positioning of its warehouses to minimize delivery time and costs across the entire network.

JMI Implementation

• JMI Collected sales and promotional data across stores and blended them to have a uniform item-by-store-by-day level of granularity.
• Standardized data is obtained by pre-processing data to de-trend, de-seasonalize, and remove holiday effects.
• Built forecast models for baseline and promotional estimations, using a combination of time series and a frequency-based approach.
• Modelled separately for regional Black Friday-type promotional events and identify the most selling and profitable categories.
• Used store-level daily sales distribution indices to break down weekly to daily sales.
• Identified similar products for new products by clustering for 10+ features (like sales, regional preference, complement, substitute, promoted group, etc.) and modelling based on them.
• Automated the end-to-end process and enabled calibration for daily or weekly updates.

JMI Implementation

• Collected large amount of customer data of its e-commerce portfolio companies from a range of data sources such as transactional/purchase data, demographic data, customer interactions etc.
• Used JMI proprietary similarity metrics with clustering techniques and decision tree to define top segments based on customer characters, geo-factors, market trends, macro-economic factors and others.
• Identified that two of its e-commerce companies have lower market penetration in top tier cities which has a high impact on its top line revenue.
• Used classification methods to score the degree of customer’s propensity for purchasing particular products of each company based on historical data obtained from the targeted market segments.
• Implemented time-series and artificial neural networks to predict revenue that is likely to result from newly identified segments and conducted back-testing using historical marketing data for validating the hypothesis.
• Cross-validated data and model accuracy using partitioned data and prebuilt ML/ AI models.

JMI Implementation

JMI’s solution comprised of the following three-phased approach:

• Data Extraction: Extracting data through web scrapping tool based on specific criteria such as company brand value, customer sentiment, product portfolio, social media posts, customer review, management team and other unstructured data.
• Predictive Analytics: By looking at relevant data patterns and trends, model can conduct early detection of the companies with high growth opportunities and decide whether it’s an opportunity worth pursuing or not.
• Deal Sourcing: JMI also helped the PE firm to identify the target companies & assisted in engaging company management.

JMI Implementation

• Assessed the client’s data preparation processes that relied on legacy PDF, spreadsheet, and CSV input formats. Used Bayesian frequency models to detect anomalies and discrepancies in input data.
• Acquired unstructured data from third-party sources like foot traffic, visitors, nearby amenities, and reviews that are likely to be associated with determining the price of the property.
• Analyzed historical data using statistical algorithms and ML methodologies to understand the rental yield, price appreciation, and occupancy rate.
• Developed a sentiment indicator tool and publicly available data (Zillow) and modeled property price and rental value on a one- to ten-year time scale.
• Developed an interactive web platform that delivers comparable analyses using a variety of different user inputs.

Solution

• Designed a solution on top of an applicant 360 data lake and replicated transactional data from the pool.
• Designed micro-batches to extract and curate features that were used to track the past performance of applicants and unique qualifiers such as the applicant’s location, past experiences, background, positivity index from answers provided, past recruitment status, etc.
• Core search engine models were trained on a regular interval (overnight batch), and trained artefacts were stored in the database. The training process used the hired status of the classifier to predict the likelihood of any candidate being shortlisted for any specific job.

• The JMI team also designed the "Search API" to take basic inputs from recruiters and retrieve the matching records. The service used AI runtime to shortlist and rank candidates further based on the probability of hiring within the context of search criteria.

Value Proposition

• The AI-driven “search API “ was able to shortlist applicants with more than 90% accuracy. The solution was tested with historical data points using a wide range of job profiles, funnels and applicants. The robust and scalable design of the solution outperformed the standard keyword and metadata-based search capabilities while offering low latency response.
• The solution helped minimize the time and effort of a recruiter to identify ideal candidates for recruitment and shorten the hiring window of hiring. It was primarily designed to support bulk recruitment initiatives where the recruiters need to qualify, appoint a large number of candidates for multiple positions and/ or locations within the shortest possible timeline.
• The multistep AI-based learning when applied on the validated history of applicants, their interview and job performances helps offer deep insights on potential hires and extends the first level of scrutiny as an intelligent hiring assistant. Such utilities not only boosted productivity significantly but also helped minimize operational cost of the recruiting agencies.

JMI Implementation

• Conducted detailed fundamental research on company financials to get insights on revenue growth, cash flows and other key performance indicators.
• Identified that company had not only outpaced the industry growth by 10% in the past 5 years, but also had an above average operating margin of 24%
• Found that the company’s next gen EUV product could extend the coverage of its customer roadmaps by 10-15 years which would help it to increase its market share.
• Identified that the company had been gaining market share for the past three years.
• Analyzed that the company has key risks from its limited number of customers. Any business failure or insolvency of one of their main customers could result in significant credit losses for the company.
• Researched the impact of current regulation on exports and found that the US government could require a license for many high-tech products shipped to China from other countries if US made components constitute more than 25% of the value.
• Based on research and management commentary, JMI found that company did not meet the 25% threshold but the US department might consider lowering the 25% threshold in some cases which could be a key risk in its future growth.
• Correlated the company’s enterprise value and EBITDA with competitors to understand its valuation with operating metrics.

JMI Implementation

JMI was involved in the end-to-end process of deal sourcing including:

• A custom-web scraping tool was built to extract data from multiple unstructured data sources based on specific criteria such as employee count, funding raised, investors, company description, company geographic location, and company sector.
• The tool filtered the results based on specific criteria such as company brand value, customer sentiment, product portfolio, social media posts, customer reviews, management team and other unstructured data.
• By looking at relevant data patterns and trends, the model conducted early detection of companies with high growth opportunities and decided whether it’s an opportunity worth pursuing or not.
• This was further refined through manual shortlisting by analysts based on investment criteria.
• Clustering analysis was also undertaken from the collected data points to further understand the geographic spread of companies shortlisted based on investment criteria. The analysis revealed that a lot of SaaS start-ups were getting set up in the states of Arizona, Texas, and Michigan.
• The shortlisted companies were added to the client's CRM portal and custom outreach emails were sent to them.
• A deck of short notes for preparation before the client's call with the prospect company management was also shared with the client.
• Promising companies for the future were also flagged, labeled and added to the CRM portal with an alarm to check on their status in 6 months down the line.

JMI Implementation

• Prepared a detailed budget model to highlight the key revenue & cost drivers of business.
• Assisted in the identification of key earnings sensitivity parameters and KPIs.
• Created returns analysis for different types of scenarios.
• Constructed a flexible model with detailed drivers and switches, enabling evaluation of potential projects and investments.
• Conducted review of relevant indicators to ensure that the deal outlay value was quantified, and that capital spending met internal funding criteria.

Methodology

• Defining the universe

Top 500 stocks from Emerging Market.

• Historical back-testing

Portfolios are checked for historical outperformance to ensure that only consistently outperforming strategies are selected.

• Research

The team does in-depth quantitative research using statistical and fundamental parameters used for constituent screening and constantly manages portfolio risk using max drawdown, skew kurtosis and other volatility parameter.

• Constituent Screening

The research team does individual stock picking after going through company reports and financials and decides on a host of qualitative and quantitative parameters to be considered while screening stocks for the respective strategy.

• Weighting

Following parameters are considered while assigning weights to stocks in this portfolio.

• Market Capitalization.
• Management quality and vision.
• Theme exposure, revenues and earnings visibility.
• Rebalance

This portfolio has a real-time rebalance schedule. On a daily basis, the research team reviews this portfolio and realign the weights with the selected asset allocation strategy.

JMI Implementation

• JMI team collected the information of 100+ US-based biotech and pharma companies and analyzed each company on the basis of parameters like market cap, FDA approval, trial efficacy, and development stage.
• Screened 30 companies with a trial efficacy rate of more than 80% in clinical stage 2.
• JMI team also analyzed the past trend and other KPIs like revenue growth, profitability margin etc. of each company and screened 22 companies that meets the investment strategy.
• Back-tested the portfolio with different weights for portfolio optimization to maximize Sharpe ratio or Sortino ratio with less volatility.
• JMI team also tracked the portfolio for a month and identified that the portfolio is trading at a rolling average of 6-11% higher relative to Dow Jones US Pharma Index in the past month.

JMI Implementation

JMI was involved in conducting initial market research covering all these aspects. Key findings of the project are listed:-

• The market cap of all listed equities in the EM is US$ 3.2tn and is amongst the top-10 traded markets globally. Equity cash market trading volume is US$8.3bn/day and derivative trading volume is US$15bn/day.
• 20mn derivative trades and 15mn cash equities trades are executed on the exchange every day and more than 10000 instruments are available for trading.
• There are more than 3000 offshore FPI funds and 500 onshore funds trading into the EM using various structures, and a fund structure could be setup within 4-6 weeks with a set up cost of US$50,000.
• The EM has tax treaties with different jurisdictions and a favorable offshore structure could be set up. Long-term capital gain taxes are 10% and short-term capital gains taxes are 15%.
• Mean latency of 120.521 microseconds with 360mn order messages per day to the exchange. Co-location and Direct Market Access is available to perform high and mid-frequency trading.
• Average daily volatility of around 3% and annual volatility of 56% across instruments offered ample high and mid-frequency trading opportunities. 10-year index return of 100% with 30% annual volatility offered ample low-frequency opportunities.

JMI Implementation

• JMI team conducted deep research on Technology, Media and Telecom sector and identified the key factors including Government and Regulatory Changes, GDP growth, Inflation and Sectoral revenue growth etc.
• Tracked the market-driven factors like liquidity, momentum and fund flow for each stock and analyzed the co-variance in relation with mean return, industry growth etc.
• Conducted detailed fundamental research and analyzed the financial model of firms such as Intel, Apple, Sony, ASML, Live Nation etc. and figured out key metrics for each company.
• JMI team tracked & maintained the latest financials and key parameters for each company and updated the model accordingly.
• Models were updated on a daily basis and any significant impact was highlighted to the client immediately.

JMI Implementation

• JMI team collected the macro-economic data like GDP growth, currency impact, crude oil price and sector trends etc. and analyzed its impact on portfolio companies.
• Conducted fundamental research on portfolio companies to get insights on KPIs and valuation ratios like PE ratios, PB ratios and, EV/EBITDA values.
• Analyzed the technical and pricing attributes like momentum and mean reversion of each stock.
• Analyzed the volatility of each stock based on historical trends.

JMI Implementation

• JMI team accessed a set of 2,000+ stocks and fixed-income instruments from the client.
• JMI developed a customized quantamental model that inherited the fundamental data of each stock, such as quarterly financials, institutional holding patterns, daily returns, etc.
• Leveraged ML model to combine core fundamental analysis with state-of-the-art quantitative techniques to predict the performance of a large number of stocks by comparing their relative performance.
• Investigated through three machine learning models using Feed-Forward Neural Networks (FNN), Random Forests (RF), and Adaptive Neural Fuzzy Inference Systems (ANFIS) for stock prediction based on fundamental analysis.
• The JMI model outperformed the standard ranking model used by the client earlier both in terms of accuracy and turnaround time as the JMI machine learning model was back tested against both long and short investment strategies for equities and fixed income assets over a long time.

JMI Implementation

• JMI team accessed the set of 5000+ stocks across three industry verticals.
• Extracted the data of each stock from DTCC which has a repository of data consisting of US Equities trade volumes in context of broader market activity on daily basis.
• Using JMI multilevel model, JMI measured volatilities, risk and returns trends by combining trends of multiple levels of trade concentration for each stock.
• Leveraged JMI model to compare the relative performance trends of industry and sector of the respective stocks.
• JMI deployed the model as a robotics process automation (RPA) to predict key metrics based on the daily feed of the data.
• JMI team filtered out the companies with ± 1 standard deviation in revenue growth relative to industry average.
• JMI team also identified that the 36 companies which are frequent in public news have high volatility and trading volume compared to industry average.

JMI Implementation

• Assessed client’s Time Series-based model and determined that biases and missing values were not handled optimally - disrupting prediction engines.
• Consolidated uncontrollable number of objective functions containing multiple lagging variables to much simpler objective functions - reducing model complexity and improving efficiency of portfolio returns using dynamic optimization techniques.
• Leveraged proprietary AI runtime to identify and eliminate survivorship, look-ahead, optimization and other biases and helped traders to transform the patterns.
• Using our trained and tested models, JMI helped clients to reduce overfitting because of fewer variables utilized - model performance stabilization.
• This resulted in reduction of false signals generated by earlier system due to missing values and data biases.

JMI Implementation

Assessed the client’s quant strategy using in-house advanced quant platform and recorded following parameters:

• Sharpe Ratio: 1.75
• Max Drawdown: 8.3%
• Success Ratio: 35%
• Annualized return: 17% over a 20-year high frequency data.

Ran the Strategy on JMI’s proprietary quant platform, across multiple sections of in-sampled and out-sampled data and recorded strategy result dataset which showed varied performance across time frames.

On further statistical analysis across time periods, JMI identified that the back tested result dataset was influenced by high kurtosis (+9.5) and negative skew (-1.7), and this led to high deviation in actual and back tested performance of the long short strategy.

Optimized the long short strategy parameters by using multi-variate regression and statistical modelling.

Added new volatility-based indicator to the strategy which led to a significant improvement in overall strategy and actual results in line with the back tested results.

JMI Implementation

• Following the client’s primary ideas on the real-time tracking and optimize entry/exit of stocks, our team conducted a detailed research and study.
• Collected 20-years of daily, weekly and monthly exchange data for all the listed samples and systematically applied approx. 30,000 stock-specific corporate action adjustments to streamline the data.
• Performed regression analysis to identify key quant driven price and volume factors for real-time tracking of all 1000+ listed stocks.
• Using JMI’s automated AI/ML platform, JMI back tested different values of the factors and developed a detailed model to track and generate multiple real-time quant signals across the stock universe.
• Developed a connector and linked the model to real-time exchange data feed which would keep models updated in real-time, and generate sound/visual triggers to monitor the markets without any human intervention.

JMI Implementation

• The JMI team understood the total long-short exposure requirement from the client and mapped it to the liquidity of various index and stock futures and options. Based on ADTV (Average Daily Trading Value) data, the team proposed to develop an index Long-Short strategy.
• Collected 20 years of tick-by-tick index trading data from the exchanges and sampled the data into 1 hour, 2 hours, daily, and weekly time series.
• Identified key price, volume, and volatility-based quantitative factors using statistical methods, developed a hypothesis, and back-tested it on in-sampled data.
• Using JMI’s automated AI/ML platform, the JMI team ran millions of scenarios based on the values of the factors.
• JMI developed an optimized strategy as per the client’s needs, which showed consistent results across timeframes.
• Re-tested the strategy on out-sampled data and risk and return parameters matched with in-sampled data and validated that.

JMI Implementation

• Deploying resources: JMI offered a comprehensive coverage expansion support for the client through a dedicated team of equity research analysts to chase new investment opportunities.
• Rigorous research: JMI performed fundamental research for in-depth analysis to build a detailed financial model and write investment memos.
• Continuous support: JMI actively supported the portfolio manager to evaluate various new investment opportunities across multiple sectors.
• Attending meetings: JMI team participated in calls with company management, sell-side analysts and industry experts to gain deep insights into the company and industry.
• New ideas: JMI team tracked non-core coverage to identify new investment opportunities
• Deliverables: JMI team developed primers, tearsheets and models along with key assumptions and worked on preparing notes updates for earnings, corporate actions, and news releases.
• Portfolio tracking: JMI actively monitored companies under portfolio coverage and updated investment thesis with the impact event on portfolio entities and update existing models a significant event.

JMI Implementation

• Public Data: The initiation coverage reports included scanning the company websites, SEC filings, investor presentations, press releases and conference call transcripts, with an aim to gain deeper insights about background of companies, products and services, business strengths, etc.
• Financial Model: JMI prepared plug & play financial models for a period of ten years (including projections for five years) which enabled us to capture the effects of various industry cycles and seasonality of the company’s businesses.
• Strategy: JMI mapped the companies competitive positioning, growth strategies and forecast capital/operating expenditure.
• Other Aspects: JMI also prepared other aspects of the reports like industry analysis, impact on sustainability, risk assessment, financial overview and company outlook.
• Analysis: JMI prepared driver-based financial forecasts, industry-specific valuation, and scenario and sensitivity analysis.
• Investment Recommendation: JMI worked on the thesis for the investment opportunity.

JMI Implementation

• Market sizing: By combining proprietary research with client insights and using our subscribed databases, we estimated a TAM for the company.
• Competitor analysis and business model changes: JMI analyzed key players in the industry from the perspective of both a competitor and a potential buyer in a divestiture scenario and also identified a recent pivot to the business model which, while not explicitly a part of the client’s original thesis, could potentially generate similar long term returns if executed optimally.
• Ownership breakdown: JMI started by understanding the ownership structure and how that would impact any potential divestiture decisions.
• Segment wise analysis and valuation: We individually broke down each business segment, benchmarked them to competitors depending on the type of product they produced and estimated their spin-off potential. For each individual segment, we forecasted future growth, came up with comparable companies, and assigned estimated future multiples.
• Mile markers and sensitivity analysis: JMI worked on identifying mile markers like market highs, continued underperformance in certain segments and Industry M&A and also performed analysis which showed asymmetric upside if management executes either opportunity (divestitures or new business model) optimally.

JMI Implementation

• Understood the business structure of two entities from the management to create a business model.
• Forecasted a 5 year quarterly model with separate a financial statements for each of the two entities and subsequently merged the financials to form a merger model.
• Calculated the synergies and impact on the capital structure of the merger entity.
• JMI team examined that the merger would result in saving operational cost of c.$4M and also bring monopoly in the market.
• Valued the company with DCF method and market based approach including company comparable (EV/EBITDA, EV/Adj. EBITDA and P/E multiples) and precedent transaction.

JMI Implementation

• Provided high level overview of sector trends, outlook and its key drivers like 5G implementation and mobile phone penetration.
• Conducted detailed fundamental research on company financial report to get insights on revenue growth, cash flows and other KPIs. Also, identified the catalyst and key risk factors with its mitigate for business.
• JMI team identified that the company will grow at c.6% CAGR in next 5 year with its ARPU increasing from 42 USD to 48 USD.
• Provided an insight on competitive landscape and constructed a benchmarking model across key metrics like market share, ARPU, and customer base etc.

JMI Implementation

• JMI team extensively reviewed the lending company from an operational, financial and market perspective.
• Identified the comparable companies with similar asset size and loan portfolio and derived the P/B multiple of comparable companies.
• Figured out the pre-money and post-money valuation at P/B multiple of 2.1x and 3.3x respectively.
• Developed pre-deal presentations including acquisition thesis and investment highlights.
• JMI also supported in investor / buyer search and involved in virtual data room management.

JMI Implementation

• JMI team identified a list of relevant target companies using an exhaustive screening process involving multiple data sources (such as Capital IQ, Factset, Refinitiv and Pitchbok), industry reports and industry associations.
• Benchmarked shortlisted companies across various key parameters including historical revenue, employee growth, funding rounds and management experience.
• Delivered company profiles of the potential 4 target companies covering key business information, segments and financial details that would help in better target evaluation.
• JMI also helped the client in scheduling the meeting with key management of target companies.

JMI Implementation

• Conducted detailed performance analysis on portfolio and found that the invested value of $250M fell 15% in value. However, dividend received during the period provided the portfolio MoIC of 1.24x and XIRR of 2.2% over 12 year of holding period.
• JMI team conducted in-depth analysis of each company and their past dividend pay out. JMI team identified that out of 18 companies, 10 companies had MoIC > 1 and positive IRR. The rest of the 8 companies had a negative return on invested capital.
• Conducted relative performance analysis with global indices. JMI team calculated the MoIC for S&P 500 and FTSE 100 would be 2.5x and 1.8x for duration of 12 years on same invested amount.
• Based on the analysis, JMI recommended the sale of 6 underperforming companies based on past performance and future growth prospects.
• JMI also carried out the liquidity analysis on underperforming companies and identified that one of their investment in the company would take ~50 trading equivalent days for sale on their exchange and two of their companies would take more than 10 days for the stake sell.
• JMI recommended the client to find a strategic buyer for the sale of one company having low liquidity and assisted the client in gradual sale of its stake for remaining 5 companies on stock exchange.

JMI Implementation

• Analyzed Energy sector’s performance, correlation of the firm’s SP with oil price, evolution of broker consensus valuation and trend of daily trading volume as a % of free float of the firm.
• Created a detailed business profile covering business overview, segmental analysis, operational presence, ownership structure, debt maturity profile, detailed financial summary with a focus on fundamental cash flow, sources & uses of funds and future capex plans, among other details.
• Prepared a football field analysis showcasing valuations based on peer comp set, precedent transaction, and analyst consensus to arrive at a range of the Company’s implied valuation.
• Created a couple of pages on the macro trends of its operational markets and Energy sector as well as created mini profiles for potential financial and strategic buyers.

JMI Implementation

• We mapped the combined geographical coverage of the two companies showcasing new markets that they would each get access to as well as mapped their combined products and services highlighting complementary offerings.
• We analyzed how valuation trends across different segments of the industry evolved over time we plotted daily rolling comps for both LTM and NTM EV/EBITDA for each sector across time.
• We generated a set of trading comps, precedent transactions as well as modelled a DCF analysis using pro-forma financials to arrive at a tentative valuation range for the combined entity.
• The analysis of synergies was a separate step from the valuation analysis and cost savings realized in historically similar mergers and used that as a benchmark to judge and analyze the possible synergies for our merged entity.

JMI Implementation

• We created a detailed profile for the company and benchmarked its key variables against regional players including factories, output, products, pricing, customers, geographical coverage among other key details.
• We analyzed overall investment cash flows by the Parent Company over the years to this regional company and estimated an IRR of c.12% that would generate based of the rumored asking price.
• Because the client had no previous experience in this region, we provided a summary document detailing key takeover rules and methods commonly used in the area.
• We also analyzed previously completed takeovers in the region to understand the disclosures and methods used in finalizing those deals.

JMI Implementation

• We were able to generate an extensive list of possible targets identified using databases, news runs, company filings of major players with asset disposal plans and PE portfolio companies where the PE is possibly at the end of its holding period and could look to exit the target.
• We further created profiles for 10 possible targets based on the final selection by our client. The profiles covered business overview, key segments, products and offerings, key developments, ownership, geographical coverage, key financials, among other details.
• We also investigated each target to check how complementary it would be once incorporated into the segment and the degree to which the target owner is willing to sell.

Solution

• JMI team worked closely with the family office and external advisor to gather all the transaction data of the investment vehicle and was frequently involved in bookkeeping for the accounting process
• Coordinated with the management of portfolio companies to get a comprehensive set of valuation reports across all its investments quarterly
• JMI consolidated partnership and portfolio accounting through one general ledger for an integrated financial picture while offering the flexibility to provide reports by individual entity, asset, investment, geography and other parameters
• Assisted the Client in NAV calculation quarterly for accounting and reporting to their regulatory authority

Solution

• JMI team first gathered the client’s accounting records for their diverse range of assets and put each into an analogous format, making them all comparable, and then integrated the data into JMI’s proprietary digital platform for our client to get insights on their asset performance across geographies
• The digital dashboard displayed the summary of the client’s assets performance, assets, and liabilities. It also provided the ability to track these assets in relation to income and expense findings, thus providing a clear representation of their profitability at any given time
• JMI team identified that their investments in the real estate assets in the European region were underperforming benchmarked to its index from the last eight months
• By taking all qualitative and quantitative data and adding in advanced technology, the client had access to a comprehensive, insightful, and intuitive turnkey solution that returned control to them

Solution

• JMI team assessed the client’s portfolio and evaluated risk across underlying assets, e.g. stocks, futures, currency, commodities and indices to build a replicating portfolio.
• Under consideration of the client’s models for possible movements of the underlying asset prices, the team simulated a large number of scenarios consisting of potential price paths.
• Using these scenarios, JMI team calculated the portfolio’s overall VaR as a percentile of the portfolio’s Monte Carlo risk distribution.
• Calculated the correlation between the different asset classes, Sharpe ratio and risk metrics to provide insights on portfolio performance.
• Monitoring the model and reporting real time, end of the day VaR on 95% and 99% fallouts to the client’s risk manager regularly.
• Computed Asset Allocation on weekly basis as per given asset allocation mandate given by family office.

Solution

• JMI conducts weekly portfolio monitoring for the company, encompassing rating metrics, valuation multiples, and cash flows analysis.
• For research purposes, JMI leverages prominent financial platforms such as Bloomberg, Zacks, Seeking Alpha, and Investors Business Daily.
• JMI creates a weekly stock screener to identify potential buy opportunities compared to the benchmark of the SPY US and DVY US Index.
• JMI develops a 'Buy Watchlist' to monitor potential investment opportunities closely. This watchlist serves as a tracking mechanism for identifying stocks with potential for purchase, ensuring that they are actively monitored and evaluated for inclusion in the portfolio.

Solution

• Business Overview: The JMI team prepared a detailed description of the company, including its history, industry, products or services, target market, competitive advantage, and overall business strategy.
• Security and Structure Analysis: The JMI team assessed the quality and liquidity of the collateral or security offered to support the financing and conducted subordination risk analysis.
• Financial Statements: Reviewed the company’s past five-year financial statements, analyzing its financial performance and evolution of the most important liquidity, efficiency, profitability and leverage ratios.
• Cash Flow Adequacy: Estimated the future cash flows modelling base case projections as well as down case scenarios to assess the company’s payment ability in adverse conditions, such as economic downturns, changes in interest rates, among others.
• Credit Analysis: Analyzed the company's credit history, checking for any defaults, delinquencies, or bankruptcies with the financial system. Evaluated credit ratings and the ability of the company to access capital.
• Management Assessment: JMI team evaluated the competence and relevant experience of the company's management team. Assess their track record, strategy and business plan, and their ability to effectively manage risks.
• Industry & Market Analysis: Conducted extensive research to evaluate the company’s position within its industry and the overall market conditions considering factors such as competition, market trends, regulatory environment, and potential risks or opportunities specific to the industry.
• Key Risks & Mitigants: JMI team identified and evaluated key risks that could impact the borrower`s ability to fulfill its financial obligations and determined the appropriate mitigants to minimize the impact of these risks.

Solution

• Data Collection: JMI team gathered data on the loan portfolio, including loan types, loan amounts, interest rates, borrower information, and repayment history. Based on historical loan tape data, we calculated a detailed month-wise breakdown of the funded principal, commissions, gross factor rates, actual collections, loss rates, and a month-wise Merchant Cash Advance (MCA) asset IRR.
• Credit Quality Assessment: Evaluated the credit quality of the loan portfolio and calculated key metrics such as non-performing loan ratio, net charge-off ratio, and provision coverage ratio. Assessed the adequacy of loan loss provisions set aside for potential credit losses.
• Financial Model: JMI team collaborated closely with Company's management team to develop a comprehensive financial model that forecasted the IRR of their MCA assets loaned out which incorporated various key inputs and assumptions.
• Portfolio Yield and Return Analysis: JMI team also analyzed the portfolio yield and return metrics to assess the profitability of the loan portfolio considering the interest income generated from loans, net of provisions for credit losses and operating costs. Evaluated the impact of changes in interest rates on the portfolio's performance.

Solution

• Data Collection: JMI team gathered information on the composition of ABC Investments' CLO portfolio. Team had identified the individual CLOs held, including their underlying leveraged loans, tranche structure, and associated cash flows. Determined the size of each CLO and its relative importance within the portfolio.
• Market Data Analysis: JMI team collected and analyzed market data, including loan market spreads, credit default swap (CDS) rates, interest rate curves, and other market indicators. Our team also keep updated on market trends and changes that may impact CLO valuations, such as shifts in credit spreads or changes in loan performance.
• Cash Flow Projection: Estimated the future cash flows from each CLO in the portfolio. Considered factors such as the interest income generated by the underlying loans, prepayment rates, defaults, recoveries, and reinvestment assumptions. JMI team also used historical data, market conditions, and credit rating agency reports to inform cash flow projections. Determined an appropriate discount rate to apply to the estimated cash flows considering the risk of each CLO tranche and the prevailing market rates.
• Monte Carlo Simulation: Team had performed a Monte Carlo simulation to incorporate uncertainty into the CLO valuation. Simulated various scenarios based on different assumptions about interest rates, default rates, and other relevant factors and calculated the range of potential CLO valuations and assess the associated probabilities of each scenario.
• Sensitivity Analysis: Conducted sensitivity analysis to identify key drivers impacting CLO valuations. JMI team also evaluated how changes in variables such as interest rates, credit spreads, default rates, or prepayment rates affected the value of the CLO portfolio. Assessed the sensitivity of the portfolio to different market factors and identify the most significant risks.

Solution

• Credit Risk: JMI team conducted due diligence on the student loan platform's underwriting practices, credit assessment methodologies, and risk management strategies. These assessment helps to mitigate credit risk by ensuring loans are extended to creditworthy borrowers based on robust underwriting criteria.
• Market Assessment: JMI team also analyzed macroeconomic indicators, employment trends, and historical loan performance data to assess market risk and establish appropriate risk mitigation measures.
• Investment Monitoring: Following the disbursement of the credit facility, our team actively monitors the performance of the student loan platform's loan portfolio. JMI team also provide regular reporting on loan delinquency and default updates. In the event of defaults, JMI team also collaborated with the student loan platform to implement strategies such as loan modifications, repayment assistance programs, or debt restructuring to help borrowers manage their debt effectively and optimize loan recoveries.
• JMI team had also provided their analysis on exit strategy based on different scenarios like Loan Portfolio sale and Renewal or refinancing for extended term depending on risk return appetite.

Solution

• Developed a comprehensive Cash Flow Schedule that includes rental income, operating expenses, interest expenses, loan origination fee, exit fee etc.
• Established a detailed structure outlining the project costs, including hard and soft costs, and how they will be allocated over the period.
• Created a financing scenario that incorporates various sources of capital, including equity, senior loans, and mezzanine loans.
• Included a provision for potential loan refinancing in the plan.
• Developed a schedule for calculating returns on equity using Internal Rate of Return (IRR) and Multiple on Invested Capital (MOIC) along with sensitivity analysis.

Action

• The system analyses limited types of data but casts a very large net – looking at a huge quantity of patients.
• The system then sorts these patients into groups and suggests a few patients from each group to a human for verification.
• The human will either accept the patient as being a valid candidate or, if rejecting, briefly document the exclusion reason in natural language.
• The system then learns, updates the entire patient set, and starts including more types of data.

Key Differentiators

• HITL system provides additive performance to existing processes rather than gains in some areas and losses in others.
• Transformer model architecture is state-of-the-art in academia. We apply it to industry applications and train our architecture on specific use cases of a client which results in the most advanced product and custom solution.

• Enrichment Strategy :

  Predictive Enrichment

• Description Strategy & Reasoning :

  Massive amounts of monitoring data can easily overwhelm medical professionals and lead to an increased time cost or a higher error rate. Machine learning can be used to highlight areas of interest in the monitoring data to show issues that medical professionals might miss. Depending on the scale, this can be extended to directly aid the medical professional in the analysis of the generated data.

• Data used :

  Data generated from at-home monitoring and reporting systems.

Problems

• These self-created checklists often contain errors which need to be corrected partway through the trial, potentially invalidating collected data and delaying the trial.
• Entering data into the web portal is often inefficient and the sites may have functionality issues which require calling the sponsor to fix.
• Data entry to the web portal is tedious and error prone.

Benefits

• Increased data quality.
• Likely high adoption rate among end users due to convenience.
• Increased likelihood of success by reduction in the amount of missing and incorrect data.

Action

• Developed systems that work with people (clients) to get data labelled and get client buy-in for UI.
• The Extraction Module then pulls information in a format that integrates with the preferred system integration.
• Leveraged bleeding-edge transformer architecture in deep learning to create state-of-the-art solutions.
• This solution is applicable not only to life sciences but also to other domains such as finance and law.

Key Differentiators

• Transformer model architecture is state-of-the-art in academia. We apply it to industry applications and train our architecture on specific use cases for a client, which results in advanced products and custom solutions.
• Our computational architecture is surrounded by spot instances and other cost-saving pieces, ensuring our clients get access to more customized and optimized solutions than other NLP players in the market.

Results

• Optimal sequencing lifted top-line sales by 2%.
• Prediction accuracy as high as 85% with affinity estimations.
• 15 - 25% improvement in customer engagement rates.
• 2 - 4% increase in sales across pharmaceutical brands.

Results

• A 1% increase in patient adherence can add up to $100 million in sales at peak.
• Referrals account for as much as 50% of prescription volume for certain specialty brands.

Results

• State-of-the-art approach to sales & marketing challenges.
• Decrease in patient discontinuation by 2%, resulting in increased revenue.
• Efficiency improvement in marketing excellence and sales training areas.
• Extension of market research team across people & location.

Solution

• Extracted data from past claim records and third-party sources and analyzed the past claim characteristics and sub-activities within a claim.
• Using data analytics, the JMI team also correlated various activities within each claim type and identified the root cause of the higher settlement time.
• The identified claim settlement time was higher than the competitors, leading to a customer satisfaction issue.
• A closer dissection of the claim settlement data revealed that not all claims took a long time, but for specific collision claims, the cycle time was almost twice as long as others.
• Collision claims had multiple features like collision only, bodily injury, glass only, other damage, etc., and the time increased proportionately with the number of bodily injuries in a claim.
• It was observed that there was a gap in the training and knowledge of adjusters that led to an increase in idle time required for verification, which was addressed, and global best practices were advised for such cases.

Solution

• Extracted structured and unstructured data from various sources including social media, public records, etc., and loaded it into a data warehouse.
• The analytics team used information across a wide variety of sources and scores the risk of fraud on multiple factors like prior conviction, multiple rejected claims, an odd combination of personal data, etc.
• Integrated social network analysis into fraud identification and predictive modelling process to generated a score and alert fraud.
• Depending on the score of a particular case, the investigators began researching more on the fraudulent claims.
• Finally, issues or frauds that were identified were added to the business use case system, to further improve the fraud detection system.

Solution

• Analyzed the historical claim data and identified the claim patterns to establish the inferences between different variables such as day of incident, time, age group and associated it with other external information such as location, behavior patterns and weather etc.
• Observed that most of car accidents happened on late Friday nights within age group between 21-24 years. JMI also examined that most of two-wheeler accidents happened on rainy season in night time.
• Leveraged AI for image processing to analyze the extent of the damaged vehicle and helped the client to generate a list of repairable and replaceable parts that were damaged in the accident.
• Using Predictive Analytics, JMI platform established the relation between variables, understood the pattern and establish these inferences to understand the propensity of claim and decide whether to skip manual intervention for claim settlement or not.

Solution

• Extracted data from past claim records and third-party sources within a geography and analyzed the correlation between claim payout and settlement time across claims of the same type.
• Observed that stronger correlation exists between claim payout and settlement time of same claim type for the client company, the primary inference is that huge or complex claims consume a long time.
• Analyzing the data with competitors, it was also found that there was no correlation between claim payout and settlement time with its competitor companies across claims of same type. It concluded that there were some other factors which impact customer retention of company.
• Observed that customers policy with stronger correlation between claim payout and Settlement time stays with the same policy for longer time. JMI identified that customers with settlement period with 5-7 days remain with same policy for more than 5 years.
• On further analyzing data with other variables, JMI team found that customer feedback on social networks is one of the most important factor on customer retention. It has been observed that ~38% of customer with negative feedback on social media leave the policy within 16-22 months.
• Helped the client to integrate social media with their Customer Relationship Management (CRM). Integrating CRM data helped client to understand customers’ behavior trends, demand and preferences.

Results

• Expedite Claims Resolution Process: 33%
• Reduction in Cost: 20%

The Challenges

• A financial company approached JMI with a research platform challenge. The application brought together market data with their own research and opinions into one view to create and evaluate their strategies.
• Their legacy implementation ran on several servers as a monolithic application, with a hand managed and complex infrastructure.
• The overall performance was poor, which is unacceptable in a trading research environment where timeliness, speed and accuracy are paramount. In addition, the ongoing cost of this setup was hard to justify.

Implementation

• We worked with the client to understand their requirements create their taxonomy and data collection procedures.
• Based on client inputs our team created a robust procedure and guidance document before collecting data for the companies.

Implementation

• We helped the rating agency create an end-to-end framework that included developing a custom taxonomy covering 1400 data points.
• We worked together with the client and built a custom model to calculate scores based on materiality and risk management framework.
• Finally, we also provided them with a technology platform and a workflow that helps them generate PDF assessment reports.

JMI Implementation

• The JMI Team reworked the entire data inflow process for data processing by building a data lake for quick and easy access to the requisite data.
• Perform extensive data pre-processing steps to convert unstructured and semi-structured data coming in from various sources into a structured, human-readable format for data processing.
• Derive intelligent KPIs based on current and historical financial data and build interactive visual dashboards.
• Built dashboards based on real estate operational efficiency to disseminate derived insights to the intended regional property managers.
• Integration of multiple utility service providers bills such as water, electricity, broadband, garbage, and sewer present across various formats such as PDF and handwritten notes on a unified scale.
• Individual property-wise, profile-rich dashboards were built based on clustering techniques. These dashboards also helped to understand tenant behavioral patterns at a granular level. Predictive analytics based on machine learning were employed to classify tenants based on their propensity to make late payments and default on loans.

Solution

The JMI team analyzed the property from various parameters which included:

• JMI team created test advertisement of the property and analyzed the response of potential tenant on Zillow to understand the demand of the property.
• Performed area and price analysis to get better understanding of nearby spots like shopping mall, tourist spots, big retailers and other points of interest.
• Understood the demographics and figured out other key parameters like population growth, unemployment rate, household income, gross rent, top employers, crime rate and historical home price appreciation etc.
• Performed Surrounding manufactured home listings of both single and multi-family accommodation, flood zone assessment, detailed unit breakdown, historical billing and occupancy.
• Performed Property tax review and tax assessments, permits and licenses review, insurance review and vendor’s list.
• Retrieved flood zone information, crime report, hurricane data, sex offenders, opportunity zone data for the target property.
• Analyzed tenant turnover, reconciled tenant rent billed and collected amount, occupancy rate, electricity and water utilization across each lot.

Solution

• The JMI Team re-worked the entire data inflow process for data processing by building a data lake for quick and easy access of requisite data.
• Performed extensive data pre-processing steps to convert unstructured and semi-structured data coming in from various sources into a structured human readable format for data processing.
• Derived intelligent KPIs based on current and historical financial data and build interactive visual dashboards.
• Individual property-wise profile rich dashboards were built based on clustering techniques. These dashboards also helped to understand tenant behavioural patterns at the granular level. Predictive analytics based on ML were employed to classify tenants based on propensity to make late payments and default on loans.

Solution

• The JMI Team through extensive research and market analysis found that properties like that were in high demand and low supply which made it a lucrative opportunity.
• The JMI team collated aerial drone shots and ground photos to present them in the deck to help better understand the surrounding locality.
• Carried out extensive market research of the surrounding area and found out the median income, largest employers, demographics of the locality.
• Analyzed the surrounding area and found out the key centers such as big retailers, shopping malls and other points of interest in and around the area.
• Evaluated comparable apartments in the area and analyzed the rents and prices of such properties to gauge the fair value of the portfolio property.
• Highlighted the Business plan of the property and listed the key business risks.

Solution

• The JMI team conducted detailed fundamental research to understand the key drivers for rental revenue and the operating cost associated with the project.
• We produced an appropriate Cap rate and projected the cap rate going forward using our in-house research databases along with calculated well thought assumptions.
• Developed a 5-year monthly financial statement by incorporating the historical numbers and the forecasted the financials.
• Performed return analysis with different scenarios and assumptions such as LTV ratio, Interest rates, Maturity and Amortization time frame, Capex expense etc.
• Evaluated and ascertained the various sources and uses of cash, total capex and operating budgets.
• Prepared a detailed equity waterfall structure and calculated the levered and unlevered IRR of the project.

Solution

• JMI team first gathered all the client’s accounting records for their diverse range of assets and put each into an analogous format, making them all comparable.
• Financial information was then integrated into JMI's proprietary digital platform for our client to get insights on their asset performance across geographies.
• The digital dashboard displayed the summary of the client’s assets performance, assets, and liabilities. It also provided the ability to track these assets in relation to income and expense findings, thus providing a clear representation of their profitability at any given time.
• JMI team identified that their investments in the real estate assets in the European region were underperforming benchmarked to its index from the last eight months.
• The platform has succeeded in consolidating information into an easy-to-use, digestible format and has improved the client’s ability to communicate with family members about their overall wealth.
• By taking all qualitative and quantitative data and adding in advanced technology, the client had access to a comprehensive, insightful, and intuitive turnkey solution that returned control to them.

Solution

• Conducted detailed fundamental research on company financials to get insights on revenue growth, cash flows, and other key performance indicators.
• Identified that the company had not only outpaced the industry growth by 10% in the past 5 years but also had an above-average operating margin of 24%
• Found that the company’s next-gen EUV product could extend the coverage of its customer roadmaps by 10-15 years which would help it to increase its market share.
• Identified that the company had been gaining market share for the past three years.
• Analysed that the company has key risks from its limited number of customers. Any business failure or insolvency of one of their main customers could result in significant credit losses for the company.
• Researched the impact of current regulations on exports and found that the US government could require a license for many high-tech products shipped to China from other countries if US-made components constitute more than 25% of the value.
• Based on research and management commentary, JMI found that the company did not meet the 25% threshold, but the US department might consider lowering the 25% threshold in some cases which could be a key risk in its future growth.
• Correlated the company’s enterprise value and EBITDA with competitors to understand its valuation with operating metrics.

Solution

• Began the search by classifying 10,000+ companies.
• Acquired the data list from multiple sources such as Bloomberg, industry associations, and trade journals.
• Filtered and cleaned the list down to 500 companies based on size and profitability.
• As per the client’s request, focused on 5 industry segments to understand the dynamics.
• Profiled 66 companies with a special focus on management and their affiliations, company financials, operations, and valuation ratios.

Solution

• Prepared a detailed budget model to highlight the key revenue & cost drivers of business.
• Assisted in the identification of key earnings sensitivity parameters and KPIs.
• Created returns analysis for different types of scenarios.
• Constructed a flexible model with detailed drivers and switches, enabling evaluation of potential projects and investments.
• Conducted review of relevant indicators to ensure that the deal outlay value was quantified, and that capital spending met internal funding criteria.

Solution

• Performed a screening for public and private entities in the renewable energy space and compiled a list based on their offerings.
• Filtered and reviewed renewable energy companies with a detailed analysis and history of all the investments and acquisitions in the sector.
• Computed their current and forward trading multiples and arrived at a range of exit multiples.
• Provided the range of valuation multiples based on the analysis and helped identify KPI enabling premium valuation.

Solution

JMI was engaged in the end-to-end process of deal sourcing including:

• A custom web scraping tool was built to extract data from multiple unstructured data sources based on specific criteria such as employee count, funding raised, investors, company description, company geographic location, and company sector.
• The tool filtered the results based on specific criteria such as company brand value, customer sentiment, product portfolio, social media posts, customer review, management team, and other unstructured data.
• By looking at relevant data patterns and trends, the model conducted early detection of the companies with high growth opportunities and decided whether it’s an opportunity worth pursuing or not.
• This was further refined through manual shortlisting by analysts based on investment criteria.
• Clustering analysis was also undertaken from the data points collected to further understand the geographic spread of companies shortlisted based on investment criteria. The analysis gave insights that many SaaS start-ups were getting set up in the states of Arizona, Texas, and Michigan.
• The shortlisted companies were added to the client's CRM portal and custom outreach e-mails were sent to them.
• A deck of short notes for preparation before the client's call with the prospect company management was also shared with the client.
• Promising companies for the future were also flagged, labelled, and added to the CRM portal with an alarm to check upon its status in 6 months down the line.

Solution

JMI was engaged in the end-to-end process of deal sourcing including:

• Provided a high-level overview of sector trends, outlook, and its key drivers like smartphone penetration and addressable market.
• Conducted detailed fundamental research on the company financial report to get insights on NIM, asset size, loan disbursal and other KPIs. Also, identified the catalyst and key risk factors with their mitigant for business.
• JMI team identified that the company AUM will grow at c.12% CAGR in the next 3 years with its customer acquisition cost decreasing from USD 10 to USD 8 and CLTV increasing up to 20%.
• Provided insights on the competitive landscape and constructed a benchmarking model across key metrics like market share, customer base, CAC etc.
• Developed a 5-year quarterly model with financial statements and valued the company with a market-based approach including company comparable (P/B multiple) and precedent transaction.

Solution

JMI was engaged in the end-to-end process of deal sourcing including:

• JMI team extensively reviewed the EdTech Company from an operational, financial, and market perspective.
• Identified comparable companies with similar K-12 STEM transactions and derived the EV/Revenue multiple of comparable companies.
• Figured out the pre-money and post-money valuation at EV/Revenue multiple of 2.1x and 3.3x respectively.
• Developed pre-deal presentations including acquisition thesis and investment highlights.
• JMI also supported investor/buyer search and was involved in virtual data room management.