How banks can increase profitability while following resilient risk management principles

Authored by Vishal Gossain, Practice Leader, Risk Analytics and Strategy and Co-contributed by Divya Tulapurkar, Senior Manager, Financial Services Risk Management, Shelly Wu, Senior Manager, Financial Services Risk Management and Jessie Wang, Senior Manager, Financial Services Risk Management

Embracing the right analytical solution to reframe risk management can help financial institutions optimize profitability and build resilience.

Now is the time for profit-based risk management

Why rethink risk management frameworks now?

There are a lot of different ways for financial institutions to assess a customer. That said, the emergence of artificial intelligence (AI) and advanced machine learning (ML) means lending decisions no longer need to be based on either risk or profitability. Technology allows us to broaden those assessments and make holistic decisions that take both elements into consideration.

Historically, financial institutions determined risk appetite through a top-down approach, grounded in a static understanding of how “good” or “bad” a customer was (i.e., a customer’s risk to default on their loan obligations). For instance, do they make timely payments and meet certain thresholds?

Now, new modelling and strategy approaches allow us to create a much more comprehensive customer view using customer 360 data, one capable of redefining exactly what makes a good customer good by understanding the potential revenue, loss and expenses associated with each lending decision. These tools enable institutions to model individual borrower decisions and then layer that model against the wider portfolio to understand how the pieces — and profitability — fit together.

Case in point: a customer who might have traditionally been considered a higher-risk borrower (i.e., does not always make payments in a timely manner) could represent an opportunity as part of a broad-based portfolio with an optimized risk-reward approach and individualized customer products (e.g., customized lines, tenures, rewards and pricing).

And the tech solutions at our fingertips now empower us with a direct line of sight to gain that understanding and make decisions accordingly. In turn, banks can help customers across the credit spectrum, including underserved customers who have historically been unable to access banking services due to financial hardship.

The ability to reverse-engineer risk management decisions around optimized profitability within risk appetite can drive a far-reaching impact right across a financial institution. What’s more, because these tech solutions can generate even better access to customer data, deploying this approach can simultaneously open up additional opportunities to personalize customer experiences, offers, services and products. That can fuel a competitive advantage in the marketplace.

By employing technology in these new ways, financial institutions can foster profitability and dial down risk while addressing the ever-evolving need to personalize the customer experience in ways that deliver value. That’s huge.

What’s the cost of standing still?

Applying AI and ML as part of an integrated, profit-based risk management solution can help maximize your profitability on every lending decision made. While upsides abound, the risks of maintaining existing, risk-based approaches to decisions can significantly hamper future profitability and growth. Put simply, institutions that don’t embrace these new capabilities could ultimately price themselves out of the market. Add in the additional lost opportunity around customer personalization, and failure to adapt now could lead institutions to lose out on market share and income.

How can financial institutions deploy tech to fuel profit-based risk management?