The NBFC Fiasco in India and AI Linkage with the Sector

The Non-Banking Financial Companies (NBFC) sector in India, which is also called the shadow banking system, provides financial services similar to commercial banks, but they do so outside the typical banking regulations. 
The trouble began with defaults by Infrastructure Leasing and Financial Services Ltd (IL&FS) since the end of August 2018. IL&FS which is classified by RBI as core Investment Company had a total consolidated debt nearing INR 1 lakh crore. Since the firm started missing its debt obligations, its ratings got abruptly downgraded from high investment grade (AAplus and AIplus) by rating agencies such as ICRA, CARE, etc. to junk status which indicates imminent or actual default status. This has stoked a fear of liquidity crunch in many of the banks, corporate and mutual funds that had stakes in IL&FS debt instruments.
The shadow banking sector is now comprised of over 11,400 firms. They have a consolidated balance sheet worth USD 304 billion or INR 22.1 trillion and are not as strictly regulated as banks. Since the biggest lenders to NBFC, the banks, have slowed down their lending to work through the USD 150 Billion in stressed assets, the NBFC has been attracting new investors. 
Their lending pace has far exceeded the banks, and several of their cream firms have received top credit ratings including IL&FS. The ratings are now in question, and the growing concern is that many of these firms may have taken excessive credit risk by lending to individuals who have little means of paying back. There are also speculations surrounding lax regulation which might have turned these firms into money laundering portals.

How Can AI Make A Difference?

The financial sector is one of the largest beneficiaries of the developments in Artificial Intelligence and Machine Learning. NBFCs are increasingly developing and utilizing state-of-the-art technology to automate their processes of lending. Everything from loan origination, customer-onboarding and loan disbursement can now be driven by AI and Big Data technology. One of the major challenges has always been tapping into unbanked sections of the economy that have no credit data or history. Once considered unsafe, they now have the potential for huge business growth.
Technology now drives the credit underwriting process which previously required a small army of people to go through the paperwork. Advancements in AI have led to algorithms for alternate credit scores that utilise customer data from social media, mobile apps data and online behavioural patterns to gain an insight into them. This offers a reliable method for psychometric scores and predictive analysis for default. AI can also help extensively in fraud detection and loan disbursements to newer segments of customers.  The role of AI can extend far beyond into the macrocosm and help in predicting potential financial crashes. Our ability to spot trouble at a distance is only as good as our ability to interpret existing data. 
Recent advances in technology have armed the industry with tools that can gather and analyse vast amounts of data in real time to help make important decisions. Keeping that in mind, the promise of AI and data analysis to prevent any future financial crisis is compelling.

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