Fintechs leverage new-age tech to minimize loan default risks

India’s banking sector has long grappled with the specter of bad debts. Public sector banks alone reported staggering losses of INR 1.07 lakh crore due to bad debts in the previous fiscal year, according to the Reserve Bank of India (RBI). A deeper dive into central bank data, as reviewed by Reuters, unveils an even more alarming picture, with Indian banks writing off over USD 30 billion in bad debt during the 2018-19 fiscal year. While these write-offs provided some relief, the underlying issue remained unaddressed. Weak balance sheets, combined with the opacity surrounding non-performing assets (NPAs), continued to haunt the sector.

The recent Yes Bank crisis has further underscored that the problem of bad debt isn’t confined to public banks alone. It has the potential to cast a wide shadow over the entire economy, impacting private sector banks, non-banking financial companies (NBFCs), and peer-to-peer lending platforms. This paints a stark picture of the limitations of traditional credit rating and underwriting methods. What’s urgently needed is a robust, tech-driven credit rating system that empowers lenders to navigate credit risks effectively and detect problematic cases at an early stage. Fintech companies can be instrumental in this endeavor by collaborating with banks and financial institutions to create transparent and auditable risk assessment frameworks. Fintechs companies use cutting-edge technology to reduce loan default risks

Optimizing Loan Distribution

Historically, banks have amassed vast amounts of customer data. However, much of this data remains unstructured and fails to contribute meaningfully to the decision-making process. This is precisely where artificial intelligence (AI) steps in. Fintech firms harness AI-powered automated tools to transform extensive datasets from various, often disjointed sources into valuable insights. Beyond traditional credit bureau data, this goldmine includes alternative data points such as a potential borrower’s educational background, employment history, daily transactions, utility and recurring payments, and even their social media activities.

With direct access to actionable insights, fintechs can construct a loan distribution model that nurtures trust, diminishes risk, and concurrently reduces acquisition costs. Intriguingly, the more data funneled into the system, the sharper and more precise these insights become. Through feedback mechanisms, ongoing learning algorithms, and deep learning capabilities, the processes of data labeling and data cleansing constantly evolve, resulting in more accurate borrower profiles grounded in their genuine creditworthiness. For instance, banks that have ventured into substantial loan disbursals with tech-driven methods like credit scoring and risk analytics engines, such as Crediwatch, have established more prudent credit disbursement systems.

Facilitating Faster Decision-Making

Human bias has historically been a significant influence on decision-making processes, contributing to loan frauds and NPAs. AI offers a pathway to streamline credit underwriting processes with minimal human intervention. By subjecting acquired data to a set of predefined rules that determine acceptability, AI aids lenders in making unbiased decisions, minimizing the chances of anomalies.

A vertical approach driven by predictive analytics empowers lenders to assess quantitative and qualitative risk factors, thereby creating comprehensive borrower profiles. Moreover, AI permits credit underwriters to concentrate on intricate aspects, like scrutinizing contingencies that might elude data analysis. Consequently, the ultimate decision still rests with the lender, but AI-based technologies expedite more precise decision-making.

In conclusion, fintechs are wielding the power of new-age technology to revolutionize risk assessment in the banking sector. By leveraging AI-driven data analysis and predictive analytics, they’re not only enhancing the accuracy of credit decisions but also making these processes faster and more efficient. In a landscape where bad debts have been a persistent challenge, these innovations hold the promise of a healthier and more resilient banking sector in India.