Predictive Algorithms Can Mitigate the chance of NPAs For Banks

Monitoring the cash flow of the business enterprise through prudent practices may be the key to go to cash flow-based lending

The Indian bank operating system is definitely burdened with the issue of bad loans. Since 2014-15, Indian banks have written off INR 5.7 trillion worth of bad loans. According to data released by the finance ministry, India’s 42 scheduled commercial banks (SCBs) collectively wrote off bad loans worth INR 2.12 trillion in 2018-19, up from INR 1.5 trillion in the last year. This accounted for 20 % of most their non-performing assets (NPAs). With regards to the country’s public-sector banks (PSBs), they wrote off bad loans worth INR 1.9 trillion in 2018-19. The exponential rise in write-offs has occurred simultaneously with a rise in NPAs. Government statistics reveal that the NPA of PSBs stood at INR 7.27 trillion by September 30, 2019.

As the issue of bad debt from large corporates has mainly arisen out of fraudulent activities (for example IL&FS, Jaypee Infratech, Jaiprakash Associates, etc.), the NPAs at MSMEs and smaller businesses have already been more driven by poor monitoring and knowledge of ground realities for such entities. The COVID-19 pandemic has further worsened this crisis despite a number of regulatory measures. Rating agency Fitch said Indian banks will continue steadily to face significant asset-quality challenges for another couple of years. From this backdrop, banks, NFBCs, and even peer-to-peer lending platforms are leaving archaic practices and poor due-diligence methodologies to technology-enabled, data-driven credit history systems. Data intelligence firms are playing an essential role in bringing this shift by offering their answers to banks and other financial institutes.

Predictive algorithms for risk monitoring

Monitoring the cash flow of the business enterprise through prudent practices may be the key to go to cash flow-based lending. It really is, therefore, vital to capture a business’s digital footprint, especially at the same time such as this when meeting the business enterprise owners personally and physically visiting their regions of operation isn’t a feasible option. Banks have to bring ‘singularity’ to all or any datasets regarding a business, including financial, non-financial, and alternate datasets. With predictive algorithms, this data can supplement and add value to the credit decision-making process. However, traditional banks have not were able to marry financial data with non-financial data to create a robust predictive algorithm model. That’s where technology firms enter into the picture. They harness the energy of machine learning (ML) to create early warning signals, thereby enabling financial institutes to lessen the chance of bad loans.

Removing human bias from scorecards

There were several instances where lenders bypassed scorecards and credit assessment models to disburse loans because they ‘knew’ the business enterprise well. Human bias is definitely a deterrent to prudent underwriting practices, which eventually increases the bad loan crisis. However, this practice is currently changing. Automated data-driven scorecards built on ML models that are customized for Indian businesses have already been deployed at many public and private sector banks for speedier and prudent underwriting. While credit history agencies claim to supply such credit file for all businesses, they still depend on manual processes. This again defeats the objective of a data-driven underwriting process. Data intelligence can only just can be found in when there can be an automated method of build the scorecard, eliminating any scope for human bias. New-age data analytics firms have not merely recognized this gap but also introduced digitals scorecards for businesses that are underpinned by ML algorithms to greatly help lenders make accurate credit decisions.

Automated UBO mapping

In developed economies including the US and the united kingdom, where traditional businesses have evolved over the decades led by a few companies, UBO analysis is definitely a location of focus. A higher tax regime and have to decrease the owner’s liability in each entity has allowed several related entities to be formed over the network. In India, such tools haven’t been deployed except only recently. Leading public sector banks are actually utilizing UBO network charts to recognize related parties, ultimate beneficiaries, and ownership matrices. The necessity of the hour is to get this done on a proactive basis for large key accounts with automated BPO mapping tools.

Data-driven on-boarding

As the Indian economy attempts to reunite to its feet, financial institutes are evaluating how they are able to underwrite and onboard clients quickly. For businesses, the necessity to pre-populate public data through the onboarding and KYC process itself has been looked into. However, looking forward to the client to submit financial statements and audited reports, which may be pulled directly from regulatory sources, is a sheer waste of time and resources. Building such capabilities straight into the KYC and on-boarding journey through advanced technologies can facilitate faster onboarding.

Alternate datasets

The GST regime in India within the last two-three years has taken about transparency, accountability, and moreover, yet another financial dataset for the lending company to judge. With user consent, this data can present a more insightful check out the business during the underwriting or refinancing process. Since there is a need to be sure parts of this data public, the foundation alone is a goldmine for credit assessment.

Taking advantage of business data

There are large volumes of transaction and business operations data sitting on any firm’s ERP systems. The question is-how does one utilize such a databases to build genuine rely upon good businesses? The perfect solution is is data lake deployments and public-private data partnership models, that may help unlock the real potential of such resources, enabling a far more detailed and accurate credit assessment process. This, subsequently, can help lenders make informed credit decisions and prevent the trap of bad loans.

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