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The Impact of Account Aggregators on Credit Risk Assessment

As the world strives for economic growth that benefits everyone, data plays an increasingly vital role in ensuring equitable access to low-cost financial products and services for both borrowers and financial institutions. To achieve this goal, banks, NBFCs, and Fintech companies are adopting innovative digital technologies to streamline the collection, verification, and insight generation of financial data from various sources.

Account aggregator (or Open Lending in some countries), a relatively new concept in the financial world, has already started to transform the lending landscape. In simple terms, an Account aggregator is a platform that allows individuals to consolidate all their financial data in one place, providing a single view of their financial health. This includes data on bank accounts, deposits, investments, insurance and other financial products.

Account aggregators have the potential to revolutionize digital lending and enable more people and businesses to enter the formal credit system. By reducing paperwork, AA allows faster access to consented data from individual customers and small businesses, enabling lenders to assess a potential borrower's credit repayment capabilities and process more loan applications in less time, without sacrificing due diligence and safety. Additionally, it can decrease the number of loan applicant dropouts by reducing the need for producing physical paperwork and creating a more frictionless customer experience.

Account Data Consolidation: Valuable Data, But is it Enough?

While data consolidation through account aggregators has been a game-changer for digital lending, it begs the question: is it enough? The need of the hour requires financial institutions to ensure effective credit underwriting. While several lending institutions are excitedly gearing up their technology to access this information, we foresee challenges in effectively using this data for credit underwriting:

  1. While statement data will be available, lenders will need to convert that intomeaningful insights.

  2. Certain service providers claim to provide statement analyzers, but these are merely aggregate variables from the raw statements. Essentially, they automate the work that one can use tools like MS Excel to do easily.

  3. Lending institutions calculate some aggregate variables and ratios and use them randomly to create policy filters in credit strategies.

  4. The industry does not provide a mature credit rating system or a credit bureau-like risk score.

  5. There is no proven methodology to use AA/Banking scores or variables inconjunction with other demographic and bureau scores to generate a composite risk score.

  6. Other than the largest banks and NBFCs, most players don’t have a system to periodically review, test, and improve these policies and scores based on AA data.

From Start to Finish: Building an Effective End-to-End AA Solution

To effectively address the concerns mentioned above, an optimal end-to-end AA solution must incorporate several key features. Firstly, it should be able to connect to the AA backend seamlessly to retrieve data in real time. Next, it should be able to analyze the data and provide meaningful insights into borrower spending capability. And finally, it should be able to consolidate the insights and provide an accurate probability of default of the customer, which can be consumed by a financial institution similar to a credit bureau score. The analysis and score must be continually validated on live borrower performance just like credit bureau scores.

Scienaptic’s Account Aggregator Product

Scienaptic's account aggregator solution combines the convenience of pulling account data seamlessly in real-time, with smart risk analysis using the years of domain knowledge of Scienaptic experts.

Scienaptic AA journey integrates with the bank’s Loan Origination System (LOS) or mobile app to provide a seamless journey from the customer. Post customer’s consent, his/her data from various banking accounts is pulled from the AA repository.

This data goes into a parser module, where the statements are analyzed and standard aggregated variables are created, such as average bank balance, credit and debit summary, etc. Next, these variables are used to calculate 75+ key ratios that Scienaptic has found to be highly predictive of credit risk, from the past 8 years of research with various lending clients.

These ratios are used in Scienaptic’s proprietary AA scorecard to provide a probability of default and the Scienaptic AA score. AA score is conceptually very similar to a credit bureau score, except that is based on a borrower’s AA information. Scienaptic AA scorecard has been tested and validated on over 8 million customers’ credit performance. Scienaptic performs regular testing and refresh of its scorecard based on predicted vs. actual default performance of customers.

Once the banking scorecard is created, it is integrated with other scorecards that are overlayed on bank/NBFC policies or rules for decision-making and eligibility. This integration ensures that the solution can deliver reliable, accurate, and personalized responses to the LOS in real time. Finally, the response is sent back to the Loan Origination System, providing financial institutions with all the information they need to make an informed decision on customer eligibility.

Scienaptic’s AI-powered platform is a suite of end-to-end credit decisioning technology to streamline and automate data-driven credit decisions. Our platform offers preconfigured APIs for data ingestion, prebuilt scorecards, state-of-the-art AI models and a business rule engine to test and implement credit policies. For more information on how you can benefit CLICK HERE.

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