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'Enter, Evaluate, Execute': Innovating Debt Financing in India To achieve business objectives, financial institutions can leverage a tech-driven marketplace & follow a 3-pronged go-to market strategy

By Robin Tyagi

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To say that the debt financing market in India is complex would be a massive understatement. One can gauge the degree of the market's said character by merely looking at the number of stakeholders within the domain: issuers, lenders, investors, regulators, policy-makers, rating agencies, trustee, borrowers and legal firms, among others. This leads to a situation where diverse product offerings, varying regulations, and changing consumer demands create an opaque environment characterized by information asymmetry, fragmented relationships, operational inefficiencies, and execution challenges.

Against such a backdrop, financial firms need to rely on innovations to diversify their service offerings and to deliver them efficiently to their target audience. By leveraging cutting-edge technology, service providers can effectively resolve issues pertaining to debt issuance, price/investor discovery, deal execution, and capital acquisition as well as investment.

To achieve their business objectives, financial institutions can follow a three-pronged go-to market strategy: "Enter, Evaluate, Execute'. Under the purview of this strategy, they can improve upon their processing as well as delivery of various debt products including – but not limited to – loans, bonds, commercial papers, direct assignment, guarantee backed issuances, asset-backed securitisation, preference shares, etc. By way of demonstration, let us take the example of securitization.

Enter: Automation and self-service

Traditionally, securitization deals are executed manually through a time-consuming process which involves numerous, cumbersome steps and touchpoints before reaching the final stage of deal settlement. This includes extensive communication between counterparties and stakeholders, sanitization, processing and aggregation of the original loan pool in non-standardised formats, selection of loans according to the risk appetite and rating requirement of investors, iterating through endless combinations of structutres and tranching levels for waterfall, drafting of detailed legal documents capturing the nunances of the issuance , and so on. Given the additional requirement of extensive back-and-forth in the course of the manual processing, even a highly efficient procedure takes around eight to ten working days to complete.

By leveraging a tech-driven marketplace, on the other hand, a client can initiate a securitization transaction by uploading two Excel sheets containing the loan pool details. The platform, by virtue of its cutting-edge design, will be able to take care of any and all pertaining idiosyncrasies including missing, extraneous, badly formatted, or incorrect data, and esoteric formats, among other things. . To work upon the user input, theycan leverage a combination of state-of-the-art tools such as online machine learning, simple as well as advanced algorithms, and processes including stemming, regular expressions matching, Levenshtein distance near matching, format bucketing, etc in order to generate standardized files and selecting loan contracts and the right levels of credit enhancement & structure according to risk profile of investors

After carrying out instant and efficient processing of the files, Investors will be able to provide feedback to the Issuers on the size of the accepted pool, acceptance and rejection of loans, along with corresponding reasons. By leveraging this process, they stand to achieve nearly a 100% accuracy in processing files with over 75% accuracy in those from new clients.

Pool selection involves cherry-picking a pool and working towards transforming it with the aim of reducing the risk factor as well as making it more attractive for investors. With tech integrations, service providers can use a simple yet effective approach to perform the pool selection within seconds of the click of a button. Investors can also explore multiple combinations of loan contracts and perform scenario analysis on final loss numbers and credit enhancements based on their selection. .

Evaluate: Analytics and data science

Today, the structuring process is manual in that it relies upon an expert who uses their experience to structure the ideal Pass-Through Certificates (PTCs). With technological aid, financial service providers will be able to leverage extensive resources and tap a vast network of publically available as well as their own proprietary data, along with complex mathematical models, classification algorithms, and heuristics to develop an intelligence-driven, automated selection and structuring process.

New-age, tech enabled solutions will be able to enable their investors and clients make informed decisions by providing them with accurate data and relevant tools such as client information, the performance of the client's past debt issuances, insights on current market trends, and so on. Using this data-driven intelligence, investors will be better placed to evaluate the creditworthiness of borrowers, along with their leverage and balance sheets. In addition, credit engine built into the platform can further augment the credit underwriting and eventually the post settlement monitoring process. Once investors make bids, the Issuer can also evaluate these bids using tools and data available on the digital platforms.

Execute: Workflows and co-ordination

By their very nature, private debt issuances are customized transactions that are fine-tuned and executed after much parleying, consisting of over 70 steps with interdependencies. Various counterparties – such as rating agencies, legal firms, auditors, trustees, clients, investors, and several internal teams – are involved in the transaction and perform relevant activities before a deal is consummated. Consequently, tracking the flow of a transaction is not only painstaking and laborious but also becomes prone to errors.

To overcome this challenge, tech-enabled platforms come integrated with configurable workflow engines and componentized UI engines which dynamically build and display the tasks and their statuses with respect to each of the counterparties.

Digital platforms can simplify the work of teams at companies, their counterparties, stakeholders, clients, and investors while optimising the coordination across teams and enhancing the task execution. By leveraging the power of data and machine learning, they can also empower their clients and investors by enabling them to perform self-service pool selection to meet their specific liquidity, pricing, and investment needs.

In addition to simplifying their securitization execution workflow, technology can enable companies to reduce their manual efforts, turnaround time, and errors by over 95%. In this way, on the back of tech-driven service offerings, financial firms can use innovation in automation, data analytics as the key to unlocking exponential scale.

Robin Tyagi

Head of Data Science, Vivriti Capital

Robin Tyagi currently heads the Data Science function at Vivriti Capital. In his current role; he has spearheaded loss modelling frameworks for complex products (securitization, direct assignment) under retails and balance sheet level credit scorecards and guarantee backed products (for wholesale loans) to ensure high levels of accuracy and has helped maintaining near zero delinquencies on the portfolio.

Robin has close to a decade of  work experience and has worked upon multiple facets of credit risk modelling for financial institutions - development of economic capital estimation models/expected credit loss models  to estimate  losses against credit defaults on portfolio and event risks for wholesale & retail balance sheet, estimation of losses and probability of default using transition state matrix based on historical portfolios , development of statistical & machine learning models to under borrower credit behaviour in various asset classes.
Additionally, Robin has worked closely with Structured Products team to gain expertise in variety of debt capital market products including - term loans, rated securitization, bilateral assignment, non-convertible debentures, guarantee-backed financing, commercial paper, long-term subordinated debt instruments and preference shares across different asset class for entire product lifecycle. Additionally; he has also developed loss estimation model & rating framework for debt AIFs- which has been used by CRISIL to rate these instruments. 
In his earlier role at CRISIL; he has also worked on variety of equity OTC derivatives product booking & payoff reconciliation like Snowballing Auto-call Notes, Binary/Digital Notes, Rainbow Option, Twin Win, Look Back, Range Accrual Notes, Barrier Options, Asian Options, Packaging notes, Customized Parsers etc; where he liaised with various internal stakeholders like traders, trade support teams, documentation teams, Quants Teams, Product Valuations Group, P&L teams etc. to sort out discrepancies between trade booking & documentation and build/test pricing models for these products 

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