“Leveraging Data for BFSI Pandemic Navigation”

Oct 4, 2023

Soon after the WHO declared a COVID-19 pandemic, there was utter chaos all across the financial world. Banks, NBFCs, fintech firms…all were hit hard by drastically pivoting market conditions and deteriorating credit quality among others. Lockdown situations in most countries and industries resulted in severe dips in cash flow with deteriorating corporate revenues and depletion of credit facilities. Governments across the world announced financial measures to ease payment pressures on individuals and businesses, such as extended moratoriums on loan payments, adding to liquidity woes.

Investors began pulling out their money, stock markets crashed along with oil prices, and central banks had to inject liquidity to keep the economy moving. Both the supply and demand sides dulled, thus impacting the economy. Uncertainty clouded investment decisions taken by investors and shareholders operating on financial markets, including securities markets.

And the problem will not go away soon. Moreover, the current downward trend could worsen which could impact the industry for years. The question everyone is asking is – what can help the BFSI industry sustain through to the other side of the pandemic?

Among other strategies, one instrument that BFSI firms can use to stir through this crisis and build further resilience is data engineering and advanced data analytics. BFSI analytics can help focus on spending patterns and customer behavior, primary transaction channels, fraud management, risk assessment, amongst others and help banks take steps from the what, to the why and finally, the how.

Risk Modeling
Poor credit quality will result in an increased number of default cases, more requests for forbearance and rising credit risk provisions. Banks need to cope with recalibrations of rating models and an analysis of credit portfolios in light of the pandemic. This will require collecting and sifting through massive amounts of customer and credit data. Analytics will process all that data at scale and perform quantitative risk analysis for better risk modeling, evaluating market risk, value at risk, accelerated credit review, and so on.

Liability Analysis and Delinquency Detection
Loan delinquency has become a bigger problem for banks during the COVID risk and will be devastating if it goes unchecked. Data analytics in BFSI plays a vital role in giving financial firms early warning predictions using liability analysis to identify potential exposures prior to a default. AI-based analytics uses drill-down reporting making it easier to detect criminal activities like fraud and money laundering by identifying transaction anomalies. Analytics helps issuers proactively use account pattern-recognition technologies and take proactive maintenance strategies by segmenting delinquent borrowers and identifying self-cure customers.

Growing Fraud in a Pandemic
The pandemic has provided the perfect storm for fraudsters to flourish, thanks to a more digital environment. Analytics sift through structured data (transactions) and unstructured data (emails, reviews, forum entries) and help BFSI companies identify potential fraud by analyzing the most recurring operational patterns regarding trades, purchases, and payments. Financial firms can use prescriptive analytics to evaluate their internal fraud control measures by looking at statistical parameters, data anomalies. AI’s high computation power will alert banks to potential fraud in payment, customer identification and so on, while ML algorithms will reduce false positives.

Credit Scoring
Various companies, especially MSMEs, are strapped for funds. They were just about making a comeback from the 2008 financial crisis when the COVID pandemic pushed them off-track once more. When evaluating them for financial support, banks typically use only credit scoring, which is not holistic and looks only at credit and financial details. This is not enough protection against loan defaulters. To determine a more valid credit score, BFSI analytics examines all available information –both structured and unstructured – using an algorithm to calculate the size of the risk the bank would take if they chose to underwrite that customer. AI-powered credit scoring models will reduce credit risk and enable decision-making and actions that are transparent and based on data.

Risk Hedging
Being able to sort out customers before they default on their installments helps banks avert disaster when the debt becomes overwhelming. Data analytics in BFSI allows banks to quickly adjust their hedging strategies across forex, commodities, equities, or fixed income as the pandemic situation evolves. They can use analytics to build portfolios and hedge risks by either setting a higher interest rate or offering a new payment schedule.

Liquidity and Treasury Risk
Liquidity stress models that were revised after the 2008 crisis are not fine-tuned to manage the liquidity crisis today, so BFSI companies need to pressure test and revise certain models. BFSI Analytics helps banks build credit line models with an additional layer of judiciousness and loan models with more flexibility to meet requirements during a pandemic. Financial firms can also use analytics to increase the flexibility of liquidity models for ad-hoc recalibration.

In Summary
To navigate the crisis brought on by the pandemic, Banking, Financial Services and Insurance sector companies worldwide must ensure that their business models, strategies and methodologies are fit for purpose and fortified with a solid recovery plan and governance models. They need to re-adjust their risk appetite statement and recovery thresholds by building a layer of BFSI analytics that can help them with:
• Managing liquidity, navigating new policies and preventing losses
• Model implementation and quick revision of risk models
• Flexible data visualization and risk analysis
• Monitoring trends and identifying emerging risks
• Insights into strategic actions
• Augmented underwriting powered by AI

In the midst of all this chaos, financial institutions have to be able to analyze new scenarios faster and learn from frequent updates to forecasts, business, funding, and capital plans. Data analytics will help companies in the BFSI sector to remain resilient and competitive in these challenging times.