Risk analytics are key to banks’ digital transformation

Market volatility and external influences are changing the way banks manage risk. SAS Australia explores how adopting digital transformation, alongside a dynamic and agile analytics-first approach, can provide banks with real-time data for identifying risks, making better business decisions, future-proofing businesses and offering a competitive advantage

The banking industry is changing rapidly, with risk at the forefront of concerns. Banks and other financial institutions are noting a rise in uncertainty and volatility within the markets, as external forces from geopolitics, cyber security, the Covid-19 pandemic, climate change and more are altering the environment in which they operate. Growing regulatory compliance measures and costs, as well as a rise in tech-forward competition, is further challenging the financial market landscape, and banks today need to find ways to remain relevant. 

Risk leaders at SAS Australia convened recently to discuss how the industry can adapt to ever‑changing market conditions. Christopher Schaub, business solutions manager – risk, believes that on-demand, high-performance risk analytics is vital for banks in confidently addressing both short- and long‑term strategies, while still meeting regulatory requirements and the demands of their customers.

Embracing digital transformation

“The industry saw the first iteration of cloud computing around 20 years ago, but it’s only within the last four to five years we’ve seen banks seriously adopting this technology for operational functions such as risk,” says Schaub. “It’s the agility and scalability to get quicker results and quicker deployment of the services into the industry. 

“On the regulatory side, we’re seeing bigger transformations around hosted services, not just from a cloud perspective, but from a big shift in outsourcing services too.”

Terisa Roberts, global lead for risk modelling and decisioning at SAS, explains how banks are currently operating in a very uncertain landscape, with emerging risks in climate, geopolitics, cyber security and more. “Along with these risks, they’re seeing heightened regulatory scrutiny leading to higher costs of compliance,” she says. “As the banks work to stay competitive, a more dynamic and agile response is needed, part of which is to provide instant, on-demand responses and decisions when risks arise.” 

Roberts notes that, by embracing digital transformation and adopting an analytics-first approach using artificial intelligence (AI) and machine learning, banks have a great deal of real-time data to use to identify risks, make better decisions and future-proof their overall businesses for the digital world. This keeps them competitive and their customers satisfied. “AI and machine learning are playing an increasingly large role in the financial services industry, so getting solid regulation and setting good standards for responsible use are a key focus for regulators,” she says. “This will allow financial institutions to implement it more and more into their analytics and solutions frameworks.”

“Banks must ask: ‘What is the value to the business overall and why should it make these changes?’ to define what they need and how they can achieve it”

Christopher Schaub, SAS

An integrated risk management strategy

Schaub explains that a phased approach is key as banks look to begin their digital transition and journeys towards an integrated risk management strategy. “It’s never going to be a big bang, it’s always going to be a phased approach. It’s important to find the quick wins along the way because you’re looking at a one- to possibly two-year project to realise an integrated risk management platform.”

The first step is assessment, which includes reviewing a bank’s existing processes around credit risk, market risk and operational risks. Banks will also need to assess their risk appetite while defining their objectives, understanding how a new risk management strategy fits into their current risk profiles. 

The next phase is quite important, as banks must lay out their business targets and what a good strategy looks like to them, a key part of which is business value. “Banks must ask: ‘What is the value to the business overall and why should it make these changes?’ to define what they need and how they can achieve it,” says Schaub, highlighting that banks can leverage their risk management investment to gain a competitive advantage in this evolving industry. 

A final part is looking at data and technology, and finding the right partner to help build an integrated risk management solution. Roberts describes the modularity of the SAS platform as a key advantage. ”Our customers can choose which modules they need at any time, depending on their current risk focus and appetite. And hosting it on the cloud means flexible processes run in a more efficient way.”

Stress-testing best practices

SAS’s enterprise risk solutions focus on integration for stress‑testing and bringing all facets together across credit risk, market risk and other key risk functions. Rather than working in silos like many legacy systems, the SAS solution takes an integrated perspective to create a robust enterprise stress‑testing framework. The SAS team describes the steps here, starting with analysing what a bank might want to stress: “The process involves aligning the testing to your business and portfolio, looking at governance and how to engage senior management while ensuring accountability, understanding the data and where it’s coming from, automation, scenario-based modelling and, then, the actual stress-testing,” says Schaub.

This phased approach is again important, as it allows the same models to be used across different areas, says Roberts. “Bringing together the risk and finance data for stress-testing is a key component to the success of this type of approach,” she adds, explaining that a phased and integrated approach can shorten the cycle times for enterprise‑wide stress‑testing.

Managing risk holistically 

We live in an interconnected world where risks are also connected, says Roberts. “If you want to get better at managing these risks, you need to get better at scenario analytics, what-if and simulation analysis, and interpreting the results.” 

Understanding this interconnectivity helps with scenario analytics and being able to analyse the impacts of various outcomes on others. Roberts explains that the SAS solution brings data from risk and stress-testing to model interconnections at a very granular level. “When aggregated, this data can show all the non-linear relationships,” she says. “With so much data available, and the added use of AI and machine learning, it’s possible to find patterns and correlations. It is then easier to start to monitor these risks.”

It is also important to encourage dialogue and collaboration between stakeholders across disciplines in an organisation, says Roberts. This helps to break down the silos, which is essential when looking at interconnected risk factors.

“If you want to get better at managing these risks, you need to get better at scenario analytics, what-if and simulation analysis, and interpreting the results”

Terisa Roberts, SAS

Fostering innovation through an analytics‑first approach

Schaub and Roberts point to several examples of how an analytics-first approach is helping banks become more innovative and competitive in this time of disruption and volatility. “The first involved implementation of an integrated risk management platform for a tier one bank, focused on enterprise stress-testing,” says Schaub. “Using a deep analytics‑based approach, the bank was able to gain insight into impairment and capital impacts, and speed its response to regulators,” he says.

Another area in which this approach is helping banks innovate, is in the emerging discipline of climate risk – an increasing focus for regulators and other market participants. Schaub explains: “Through an analytics-first approach, firms are developing a more granular view of climate risk vulnerabilities and the impact on customers and portfolios. They’re learning how best to protect themselves and the global community from these growing risks.”

Resilience in volatile markets

Roberts cites the pandemic as a real stress event, which rendered most risk models completely void overnight. “During this time, customers were able to use the SAS solution – the what‑if and simulation analyses – to run daily stress tests to see what impacts they could expect from the pandemic,” she says. “The risk management component allowed them to be more prepared for any potential impacts that could arise.” 

A further case involves an Asian bank that was able to automate its small and medium-sized enterprise (SME) lending process. “This led to better internal models and a dramatic increase in revenue through higher straight-through processing rates,” Roberts says. “And, by using AI and machine learning to underwrite these SME loans, the bank was also able to extend its models to other regional banks and partners.”

These examples demonstrate the key advantage of using an AI and analytics-based solution. “Effective risk management is much more than a box-ticking exercise,” says Roberts. “It’s about creating value for a company and giving them a competitive advantage in an industry that is continuously evolving due to technological advancements.”
 

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