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Is your bank continuing to rely on traditional risk management approaches to fraud, compliance and other critical tasks – potentially exposing itself and its customers to severe financial and reputational consequences? Can your bank capture, process, analyze and utilize data effectively to improve its risk management function? If not, it may lose traction and customers and ultimately become irrelevant.
Risk management is a perennial problem for banks, spanning every aspect of their operations, and presents obstacles ranging from regulatory change and changing customer expectations shaped by digital, mobile and online technologies to cybersecurity, fraud & identity theft, and competition from incumbent players and new entrants including fintechs.
Inadequately addressed, any of these challenges can cause severe business, reputational and financial damage, and many banks continue to struggle with legacy systems that do not enable them to effectively manage risk in an increasingly dynamic environment. For example, these labor intensive, legacy systems can require banks to take 10 days or more to develop the capability to combat new types of fraud, which leaves their assets, customers and potentially employees exposed and vulnerable during that period. Furthermore, many traditional systems also operated on limited and imbalanced fraud samples that generated insufficient data to support accurate fraud modelling, leaving the bank open to the direct and indirect costs of exposure to and remediating fraud.
Pressure to improve fraud risk management in particular also comes from regulators that have consistently reminded banks of their obligations in this area. For example, the Office of the Comptroller of the Currency, the United States bureau that regulates and supervises the country’s banks, issued a bulletin in 2019 reminding banks that sound fraud risk management principles should be integrated into a bank’s risk management systems, and added that banks’ fraud prevention and detection tools should evolve and adapt to remain effective against emerging fraud types. The office also reminded banks that fraud was not just a matter for security or technology teams, noting that senior management should understand a bank’s exposure to fraud risk and associated losses and use this information to monitor and manage the area, while the board should receive regular reporting that enables them to understand the bank’s fraud risk profile.
So how can banks transform their risk management capabilities to meet these new demands? The answer lies largely with data, and data analytics, machine learning and AI are key to modernizing the risk management function at banks – a perspective reinforced during the pandemic when, a recent report into Chief Risk Officer perspectives by professional services network EY reveals, many banks turned to new data or different data sources to manage risk as existing risk management models were unable to support the decision-making required. Partly as a consequence, nine of 10 banks planned to broaden risk skill sets, with leveraging data a key focus for risk professionals.
So how can technologies and products that utilize or enable analytics, AI and machine learning help banks improve their risk management capabilities? The answer lies in part in helping banks identify deeper relationships between economic, market and regulatory factors and their own performance in areas such as operations, finance and regulatory compliance. This helps them build more resilient, accurate models that address key areas of risk, including credit risk, card fraud and illicit or non-compliant ‘rogue trader’ behavior.
In particular, machine learning that uses a combination of supervised learning (models trained using data to produce output for a given input) and unsupervised learning (techniques that can detect patterns in data without any information about ‘right’ or ‘wrong’ groupings) enables banks to remain on top of rapidly evolving risk factors.
Systems that utilize data, AI and machine learning enable banks can generate risk scores informed by vast volumes of transaction data for digital business activities in milliseconds, with continued learning and training enabling them to keep pace with dynamic, escalating fraud attempts.
This capability is particularly important at a time when managing and mitigating risk across a range of fronts has never been more important. For example, recent research conducted by LexisNexis Risk Solutions points out that average monthly fraud attacks on financial services and lending firms of all sizes and types increased significantly during the pandemic – and more of those attacks than ever before were successful. Mid-to-large digital financial firms experienced an increase of nearly 40% in successful attacks since before the shutdown, while mid to large digital lending firms suffered a rise of nearly 30%.
However, the role and value of AI solutions to banks is far broader than managing risks associated with fraud, credit and traders, with benefits awaiting institutions that choose to apply AI to areas such as cybersecurity and customer expectation risk management. For example, digitalizing cybersecurity documents with AI can streamline and automate incident case management, while technologies and applications such as robotic process automation, machine learning and natural language processing can help automate manual cybersecurity tasks, improving efficiency and effectiveness.
AI-powered solutions can also help banks personalize interactions with consumers and by automating repetitive tasks, free team members to design and deliver higher quality customer experiences.
Furthermore, AI, machine learning and data analytics can enhance compliance risk management, a function and requirement that Citigroup reportedly estimates costs the banking industry $270 billion per year, or 10% of operating costs. With the right products, tools and technologies, banks can identify correlations that may otherwise go unnoticed by internal analysts – enabling them to get ahead of compliance risks and avoid any resulting reputational or financial consequences, and by automating processes and delivering insights, reduce compliance personnel costs. This is a particularly important capability, particularly for multinational banks subject to regulatory regimes that vary between countries and continents.
Overall, banks are increasingly aware of the potential of machine learning and AI to reduce the cost of risk management – EY notes that while its past surveys found the cost of risk management was continuing to rise, 29% of respondents to its latest version planned to reduce costs, “pointing directly to the increased use of technology and data.”
Of the Chief Risk Officers surveyed by EY, 88% listed process automation (including intelligent automation) as an area senior management had targeted for digital transformation, followed by modernizing core functions and platforms (listed by 66%), customer insights driven by advanced analytics, AI and machine learning (64%) and cloud migration and adoption (63%).
So what are the key characteristics of a solution that can help banks fully realize their risk management capabilities? Rather than consider piecemeal solutions that address only elements of risk management, banks should consider solutions that can deliver end to end capability to solve multiple risk management problems using a combination of technologies including AI and machine learning.
A cloud provider with solutions proven in complex financial services customer environments is the answer for banks looking to improve their risk management. Alibaba Cloud provides a range of proven AI-based solutions that can help banks manage risk across aspects of their operations, including the Alibaba Cloud Fraud Detection risk control platform that uses machine learning algorithms and stream computing to identify frauds in core services, and enables institutions to protect accounts and evaluate risks associated with complex credit applications.
In addition, Alibaba Cloud Digital Credit Lending enables institutions to develop, train and deploy credit risk and fraud risk models to determine digital lending applications in real time, using machine learning solutions such as optical character recognition and natural language processing in concert with data warehousing, machine learning drag and drop tools and an AI-powered Decision Engine. Furthermore, solutions such as Alibaba Cloud RPA can help automate and improve the speed and accuracy of tasks associated with cybersecurity and delivering a high-quality customer experience.
These solutions can help banks – and other financial institutions – deliver the dynamic risk management capability integral to relevance and success in the modern environment.
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