Tech can address loopholes of traditional credit models

Sandeep A, Co-Founder & CPO, Crediwatch, says that despite the tremendous progress it has made in recent years, FinTech’s disruptive power is yet to be fully exploited, especially in the credit risk sector.

How is the financial industry handling the absolute data explosion that has taken place in the last couple of years? There are also issues of security, data privacy and residency. Are we able to cope?

The Financial Technology industry has been on growth trajectory over the past few years because of a supportive environment by the RBI and government policies. Given the effort to digitize several information sources by the government has helped bring transparency on businesses, Fintechs have emerged to capitalize on this data explosion. Unfortunately, several Fintechs still look at becoming a data aggregator and add little or no value to the datasets itself–leading to increased efforts by the ultimate consumers. The real value-add, hence, is in bringing insights as a solution.

Another game changer was data protection bill that came in early this year, even though it might take a while for FinTech companies to adapt to the new data protection guidelines. A quick makeover won’t suffice; they must make continued efforts to build a robust privacy system for storing and processing of personal data. Despite the initial hiccups, however, the Personal Data Protection Bill, 2019 can be revolutionize FinTechs wherein they can derive immense value from free sharing of data between the customer and the service provider as a result of newfound end-user comfort due to the proposed bill.

In what ways can Artificial Intelligence-Machine Learning tools help the financial services industry? Can they reduce credit risk?

We realized that focusing on quality risk and business insights using a proprietary AI based predictive engine is the way forward for financial services industry and this has helped us differentiate ourselves as a leading analytics player. At Crediwatch, we employ AI/ML algorithms on alternative data points such as statutory payment statuses, litigations, media sentiment, GST invoice data, bank statements as well as traditional data points such as financial ratios, industry outlook etc. We have completed the development of the enterprise version of our flag-ship product, Early Warning System. This product is compliant with the RBI framework and is based on a proprietary library of 190+ early warning signals. The system comes with a case management module to track each alert and manage post-alert actions from the respective portfolio manager.

What about predictive analytics? What is the tech behind that?

Human bias is a major influence on the decision-making process that has previously resulted in loan frauds and NPAs. AI can streamline the credit underwriting process with little to no human intervention. By running the acquired data against the set of rules that are designed to determine acceptability, it helps lenders to take an unbiased decision that eliminates the scope for any anomalies or discrepancies. A predictive analytics-driven vertical approach enables lenders to analyze quantitative and qualitative risk factors to create a comprehensive borrower profile for assessment. Additionally, AI allows credit underwriters to focus on more complex aspects that like looking into other contingencies that the data may not reveal. Hence, the final decision ultimately lies with the lender, but AI-based technologies facilitate more accurate decision making in a much faster manner.

What are the data tools used in all of the above?

FinTech has rapidly evolved beyond its early stages to offer a broad array of financial products and services. From net banking to mobile phone payments, peer-to-peer lending and automotive insurance, FinTech is offering enhanced capabilities, convenience, or lower prices and fees. Despite the tremendous progress it has made in recent years, FinTech’s disruptive power is yet to be fully exploited, especially in the credit risk sector. By leveraging advanced AI applications such as machine learning, deep learning and predictive analysis, FinTech startups can help banks to get more insights from available data, evaluate loan applications and minimize defaults.

How easy is API integration, now that so many services and parties are involved?

The Crediwatch platform allows for seamless integration of its data and services through API. Clients can either ingest our data and analyzed scores via API into their internal systems or use API calls to request for reports from our engine. Several banks and NBFCs have requested to integrate our solutions with their LOS and LMS helping them have a seamless experience. Also, the Crediwatch platform has capabilities to ingest data from the bank’s CBS (core banking solution) as well in order to merge both public and private data for a comprehensive analysis. This has already been implemented for our Early Warning Signals solution.

What is the future of Fintech and Insurtech? What trends are we likely to expect in the coming future?

Technology can address the loopholes that exist in traditional credit models, whether in terms of data, process automation, or prediction of loan defaults. Large Indian banks like SBI, Indian Overseas Bank, Karur Vysya Bank, as well as many NBFCs like Aditya Birla Finance and CapitalFloat, have also successfully employed the solutions provided by Crediwatch to strengthen their AI capabilities as well as their decision support systems.
The advent of open banking and the implementation of standard protocols in most of the BFSI companies as well as the increased integration with external systems will further help build rich datasets which FinTechs can use for development of AI and improve predictions.