As the risk of cyber threats and liabilities looms over the banking industry, many are turning to emerging technologies such as artificial intelligence to manage growing concerns. Fortunately, this adaptivity may effectively quell high-level risk management and regulatory compliance concerns.
Business Wire recently shared findings from the 2024 Regulatory & Risk Management Indicator survey by global information services company Wolters Kluwer. The survey indicated that, while almost half of the respondents expressed concerns in the US banking industry, those concerns are markedly lower than in previous years. The 2024 Indicator Main Score dipped from an index level of 119 in the 2023 survey to 99 this year. Although this is seemingly a slight change, the 20-point difference brings the numbers closer to 2022 levels. It was driven by fewer major banking regulations being introduced compared to 2023. As such, more respondents expressed increased confidence in managing regulatory challenges.
The survey also reveals that manual compliance processes remain the biggest obstacles to maintaining effective compliance programmes. 31% of respondents deemed technology an essential aspect of automating regulatory change management programmes, despite 42% stating they still often rely on manual processes. Still, organisations are optimistic about integrating automation and artificial intelligence technologies, with 64% anticipating accelerating automation investments for digital lending and 63% for automating regulatory change management systems.
Meanwhile, researchers emphasise the significance of employing technology-driven solutions to address common burdens, from interest rates to ransomware attacks and operational resiliency.
Risk management and technology
In the past few decades, the banking industry has experienced failures and financial crises that have had long-lasting effects on global economies. Given the industry’s power over creating and managing money for individuals and institutions, banks must identify and mitigate potential issues that may stem from their operations and investments. This process is called risk management.
Risk management consists of identifying, assessing, monitoring, and mitigating various types of risks. In the banking industry, these potential risks include:
- Credit risk
- Market risk
- Liquidity risk
- Operational risk
- Reputational risk
Risk management may be more challenging these days due to evolving technologies and regulatory requirements in response to other socioeconomic factors. To adapt, organisations in the banking sector must develop and implement policies, procedures, and frameworks to better manage risks while staying in compliance with shifts in regulatory demands. Risk management also plays a significant role in regulatory reporting and stress testing, allowing banks to gauge their organisational capabilities in the face of potential adverse scenarios.
Below, we’ll take a look at some of the most common forms of risk management for banks and how integrating tech can help:
Credit risk
One of the most common types of risk management for commercial banks is credit risk. As banks loan out large amounts of money to companies and businesses, there is always a risk that a loan recipient will be unable to return the money, resulting in credit risk. Unmanaged, this can lead to interruptions in cash flows and increased collection costs.
To mitigate this, banks and investors rely on credit risk assessment tools. Most recently, MSCI Inc. and Moody’s Corporation joined forces to launch an independent risk assessment tool for private credit investments. The first-of-its-kind initiative aims to enhance transparency and bolster investors’ strategies for asset allocation in the private credit sector. Moody’s also plans to expand its flagship EDF-X models into MSCI’s private credit solutions, offering in-depth risk insights and early warning signals for potential credit risks.
Market risk
Market risk refers to the risk of losses on financial investments caused by price movements. Several market factors attributed to this form of risk include recession, rising inflation rates, and unexpected changes in equity markets. Therefore, banks involved with investing in capital markets or sales and trading are more vulnerable to this risk.
While digital market risk solutions are available, banks rely on regulatory changes to help mitigate market risk. In 2024, the introduction of Basel 3.1 updated previously standardised approaches for calculating various risk factors. While the Prudential Regulation Authority (PRA) ended up delaying the implementation of Basel 3.1 standards, the new implementation would involve additions to the framework to consider additional risk factors that may impact market volatility and, subsequently, market risk.
Liquidity risk
For banks, liquidity risk is the ability to access cash to meet funding obligations, allowing customers to make deposits and provide cash in a timely manner. Liquidity risk management is especially crucial to prevent a potential bank run-out of panic, which happens when too many bank customers withdraw their money simultaneously.
To mitigate this risk, banks rely on asset/liability management (ALM) solutions to manage the use of assets and cash flows in the organisation, reducing the risk of loss from not paying liabilities on time. Wolters Kluwer’s OneSumX software handles ALM by coordinating interest rate risk, liquidity risk, funds transfer pricing, and capital management requirements. The OneSumX ALM also facilitates various aspects of risk management, offering flexible balance sheet modelling, stress testing, and dynamic planning by providing a holistic view of local and global risks based on a single data source.
Operational risk
Aside from market factors and customer demands, banks should also prevent errors, interruptions, or damages caused by people, systems, or processes from potentially incurring losses. For banks that deal with sales and trading, operational risk levels are higher than those of simpler operations such as retail banking and asset management.
Internal fraud and transaction mistakes are common operational risks impacting banks. In an increasingly digital age, system glitches and downtimes pose some operational risk. Operational risk also refers to potential cybersecurity breaches, which may lead to fraud and loss of critical data and information. Today, the Bank of England works alongside financial authorities, such as the HM Treasury and the Financial Conduct Authority (FCA), to ensure the country’s financial sector remains resilient to operational disruptions.
Reputational risk
Finally, in addition to financial and data losses, banks should also invest in assessing their reputational risk. In banking and financial services, reputational risk refers to the possibility of consumers and stakeholders developing a negative perception of the bank. This applies to individual branches of a bank and the entire brand and organisation following a specific event. An example is the Wells Fargo scandal of 2016, which involved millions of unauthorised customer accounts being opened to meet sales quotas and bonuses.
The reputational damage led to fines and investigations at federal, state, and local government levels for the bank, as well as over $3.2 million (£2.39 million) spent on customer refunds, on top of a $142 million (£106.13 million) class-action lawsuit. For banks to mitigate reputational risks, it’s important to establish transparent and open communications with customers and stakeholders. For example, many banks and financial institutions today invest in setting up a digital and online presence for better information dissemination among customers.
The benefits of AI integration for banks’ risk management
The 2024 Wolters Kluwer survey shows that more banks and financial institutions are investing in automation and AI technologies to help boost risk management programmes. According to the Bank of England (BoE), advanced AI models, including generative artificial intelligence models, can provide rich insights based on new data. This allows the AI to produce complex outputs and facilitate better organisational decision-making. The BoE also predicts that in the coming years, various parts of the UK’s economy will benefit from continued developments in AI technology. For now, automation and AI are widely used to help reduce resources spent on routine administrative tasks, freeing up employees’ time for higher-value work. Case in point, major banks like Citi, HSBC, Deutsche Bank, and JPMorgan Chase use AI to automate fraud detection across banking activities. This, in turn, ensures that banks enjoy more long-term, productive economic growth and better customer satisfaction.
AI (particularly generative AI) could also be used to create risk management systems that provide automated reporting, improve risk-related decision-making, and partially automate drafting and updating policies and procedures based on shifts in regulatory demands. This level of automation would enable risk management professionals to make more accurate decisions and offer protocols in case of high-risk events.
So far, industry experts predict that proper and responsible use of AI in the sector can enable better fraud detection, credit risk assessment, compliance, operational efficiency, and improved customer experience.
Why banks are still struggling with AI adoption
While using artificial intelligence offers numerous benefits for banks and risk professionals, there may also be additional risks to consider. For example, some more complex AI models may introduce challenges regarding predictability, explainability, and transparency of model outputs. AI adoption also requires banks and financial institutions to invest in training staff and employees to prevent errors and other operational mistakes.
Additionally, banks may struggle to transition their legacy systems to modern technologies. An IBS Intelligence survey notes that 55% of their existing core solutions are the biggest roadblocks to achieving their digital goals. From this survey, only 32% of banks have successfully integrated AI into their core systems thus far, with 27% saying they planned to adopt AI within the next two to five years.
These logistical and technical hurdles associated with effective AI adoption may be getting in the way of speedier adoption by UK banks and financial institutions. New research from Evident Insight highlights that British banks are still “playing catch-up” to their US and European counterparts regarding AI adoption. The research calls for “stronger investment and a more cohesive AI strategy at a national level” for UK banks to truly catch up. More importantly, the Evident Insight data showed that only a third of the AI graduate talent from UK universities working in banking works for British firms. At the same time, researchers point out that banks actively investing in AI are “also hiring more across the board”, suggesting that “AI-driven banks are growing, not shrinking”.
Even outside of AI adoption within banks and organisations, the industry should still consider the potential misuse of the technology by bad actors and at a consumer level. A recent Reuters piece details rising concerns that AI may be used to generate fake news. This is supported by findings from a Say No to Disinfo study indicating that increasing amounts of fake news on social media heighten the risk of bank runs. The UK research company implores that financial institutions should improve monitoring protocols to promptly detect disinformation that may impact customer behaviour and pose reputational risks.
Meanwhile, banks and regulators are increasingly concerned about bank run risks exacerbated by social media. This is in light of the Silicon Valley Bank collapse in 2023, during which depositors withdrew $42 billion (£31.39 billion) in 24 hours.
Of course, the adoption rate of AI can also significantly improve for UK banks if a more favourable regulatory landscape is created. The government recently introduced the “AI Opportunities Action Plan” alongside the US Executive Order, “Removing Barriers to American Leadership in Artificial Intelligence”. Both initiatives have helped create a regulatory framework that would be more AI-friendly and focused on innovation and growth.
Where the banking industry is headed
Moving forward, banks and financial institutions in the country will continue to adopt emerging technologies to mitigate potential risks better and adapt to changes in consumer behaviour. In a previous post, we explained Mesh‘s acquisition by Trabian Technology. The strategic acquisition helped strengthen Trabian’s offerings to community financial institutions and technology companies. By acquiring Mesh, Trabian can incorporate the company’s comprehensive banking infrastructure platform to help financial institutions and fintechs operate more efficiently and innovate faster. This includes improvements to Trabian’s software development, platform integration, data automation, and web development for over 200 banks, credit unions, and fintech clients.
Beyond AI and risk management, experts predict that future movements in the banking industry will include further growth in the digital banking sector, thanks to widespread Internet connectivity and mobile banking. Even high street banks are dipping their toes into digital banking to remain competitive and offer services without physical branches. Experts also point to changes in the country’s regulatory environment, with a sharper focus on matters such as consumer protection, data privacy, and AI developments. This includes an increased focus on implementing the Basel standards, which aim to improve the UK financial system’s resilience against future and potential crises.
Meanwhile, the continually growing fintech market will significantly bring banking and essential financial services to younger consumers. Industry experts note that the more tech-inclined demographics have demonstrated a preference for experience and convenience over legacy brand loyalty, which means that bigger banks and financial institutions will need to invest even more in their digital offerings to satisfy this change in generational preference.
The shift towards customer hyper-personalisation aligns with improvements and growth in the fintech market. This adds another layer to the need for banks to innovate and adopt new technologies, as consumers now prefer that banks tailor specific products and services to unique and personal situations, ensuring less friction between users and financial institutions. This may mean a considerable shift away from traditional banking and financial services due to differences in generational aspirations. For example, younger consumers may be more inclined to seek credit card promotions for big holidays instead of loans for their first homes. Of course, this also means that banks and financial institutions must balance responsible data gathering to understand their customers with stringent data protection protocols to prevent data theft.
Last but not least, there will likely be an increased focus on talent retention and development in the UK’s banking industry, especially as tech adoption continues to rise. Fortunately, banks around the country are already investing in talent development initiatives. The Bank of England, for example, has the Future Talent programmes designed to equip future banking talent with the necessary skills and knowledge to start and keep a career in the UK financial system. This includes apprenticeships, internships, and graduate and PhD programmes for all talent levels.
Conclusion
Banks in the UK and worldwide continue to adopt automation, artificial intelligence, and other emerging technologies to help manage risk management and regulatory compliance concerns. Wolters Kluwer’s recent Regulatory & Risk Management survey indicates progress in this matter, as the numbers are lower than in previous years, thanks to fewer major banking regulations being introduced in the year.
Beyond automation and AI, banks and financial institutions rely on various technological and digital risk management solutions to monitor, assess, and mitigate industry-wide risks, from credit to operational and reputational risk.
While effective AI integration and tech adoption in banks’ risk management programmes can benefit them, the adoption rate is still considerably low in the UK compared to regions like the US and Europe. Still, a future with more supportive regulatory requirements and tech-inclined government support may bode well for the UK’s banking industry.
To navigate the fast-paced world of risk and liabilities, banks must continually adapt to meet growing customer demands. Outside of technological upgrades, this also includes essential investments in talent development and retention and a deeper understanding of new and younger generations of banking customers.
