As markets have changed and evolved with a global pandemic, threats of war, and political unrest, more banks are increasingly concerned with risk. Assessing risk is no longer a guessing game, but one powered by AI and machine learning. This type of analysis lets banks deal with their risk in a more intelligent and data-driven way through risk analytics. Here’s how.
Risk Analytics and Policy
First, risk analytics enables banks to set policies in several areas. In banking, there are three primary areas of risk: credit, market, and operational risk. In each of these areas, banks establish a fluid policy.
For example, in the market risk category, risk analysis tells them what investments to buy or sell at any given time based on market factors, and what assets to hold even through down markets in anticipation of a recovery.
Setting policy is just one area where risk analysis comes into play.
Credit Risk
When an individual applies for a loan or any kind of credit, a process called underwriting begins This process can start with an automated screening based on credit scores, payment history, and debt to income ratio. However, other factors go into credit risk, including geographical locations, the borrower’s occupation, and more.
Historically, credit risk analysis has occasionally been plagued by human bias. Automated risk analytics has the potential to mitigate that issue and create a system of analysis that is not only faster but fairer.
Credit risk is also about the bank’s assets and how they are leveraged as well, and risk analysis can help banks make better decisions about what credit to pay off, what to maintain, and when to increase cash reserves rather than borrow.
Credit risk analytics makes banks more stable, solvent, smarter, and more efficient. But banks also face other risks beyond their control: that of the market.
Market Risk
Over the last few years, the market has been a volatile place, with stocks moving up and down in often surprising ways. “Pandemic era” stocks surged and then fell. Historically stable companies and many good market investments have fallen in value.
Market volatility is always a risk and can be impacted by everything from interest rates set by the Fed to scandals, energy demands, natural disasters, and more. “It’s hard to predict things, especially when it comes to the future,” Neils Bohr famously said. It’s held true when it comes to real estate, bond, and futures markets as well as the major stock exchanges.
risk analytics can help banks understand the potential impact of market and external forces, and make better decisions about how and when to invest, protecting their future market value.
Operational Risk
Operational risk is defined as: “the risk of loss resulting from inadequate or failed internal processes, people, and systems or from external events.” In other words, risks such as employee loss and theft, natural disasters, robbery, and other factors and events.
This means banks must look at every aspect of physical risk, starting with the hiring and retention process, operational security, incident preparedness training, and more.
While every operation comes with some risk, banks can be especially vulnerable to things like cyberattacks, theft, and fraud. risk analytics software can help by modeling certain scenarios and enabling the development of preventative solutions before a problem occurs.
Taking every factor into account, banks can prepare themselves for the worst-case scenario, evaluate security risks, and put response plans in place.
Predictive Analytics
With so many areas of risk, banks must undertake risk analysis. And while predicting the future is hard, it is possible to predict trends, establish probabilities, and act on that information. That’s what predictive analytics is all about. But it goes further than that. There are four areas where predictive analytics can help banks.
- Credit scoring. As mentioned above, credit scoring is itself far from perfect, and predictive analytics can enable better analysis of actual credit risk tomorrow rather than just today or historically.
- Fraud detection: One large area of expense when it comes to banking risk is fraud. Early detection can lower the risk of fraud or prevent it altogether, revealing where systems and even individuals might be vulnerable.
- Collections: Rather than a blanket collections policy, predictive analytics can help banks segment truly risky customers from those who present little to no long-term risk.
- Cross-selling: On the positive side, predictive analytics can tell employees who are likely to open new lines of credit or use other banking services. This increases customer engagement and sales conversion rates.
risk analysis is essential to every single business out there. Intelligent RISK ANALYTICS WITH A PARTNER LIKE VENTIV means better decisions, a more efficient business, and greater profits. If you’re looking for a risk analysis solution, contact Ventiv today.
