Industry
Banking
Banking Services Transformation: Unlocking Exponential Value
Highlights
- A proactive approach is critical for effective risk management in the banking, financial services, and insurance industry.
- Banks and insurers need continuous access to real-time risk intelligence to proactively identify problems and prevent them from exacerbating into major issues.
- Adopting GenAI will help BFSI firms to transform their risk and compliance function into a proactive and preventive model that will help build long-term resilience.
In this article
Introduction
For risk management to be effective in the banking, financial services, and insurance (BFSI) industry, a proactive approach is critical.
Reacting to events after they occur often results in both financial and reputation loss. Proactive risk management involves anticipating risks, implementing measures to preempt them, and constantly monitoring and adapting to evolving situations in real-time. It demands a continuous flow of risk intelligence to enable banks and insurers to identify issues at an early stage and develop and implement strategies to prevent them from blowing up into significant problems that cannot be easily controlled or mitigated.
Maintaining a steady flow of risk intelligence, however, necessitates running complex analytics on large datasets to detect patterns and anomalies, anticipate threats, and implement controls, which in turn ensures a more resilient and trustworthy financial system. Given the volume, velocity, and nature of the data that is continuously generated, BFSI firms face challenges in performing analytics and deriving intelligence in time. And here’s where generative artificial intelligence (GenAI) can help.
In fact, BFSI firms have realized this—the Celent survey GenAI-oneers in Risk & Compliance: Cross-Sector Survey and Spotlights by Research Director, Neil Katkov, reveals that 59% of firms across financial services are implementing or testing GenAI use cases for risk and compliance. Furthermore, 53% of firms across banking, insurance, wealth management, and capital markets foresee high or moderate impact from GenAI on risk and compliance over the next two years.
While leveraging GenAI can help tremendously in facilitating proactive risk management and driving compliance, it is not without pitfalls and risks. Successful implementation will depend on adopting a balanced approach after a comprehensive cost-benefit analysis.
The art of the possible
GenAI is set to revolutionize the risk and compliance function in the BFSI industry by enhancing the accuracy and efficiency of risk assessment and mitigation strategies.
The most promising areas for GenAI proofs of concept (PoCs) are seen in fraud detection, followed by operational risk, enterprise risk, and regulatory change management and reporting. Embracing GenAI will allow BFSI firms to transform their risk and compliance function from reactive, issue-driven action to a proactive and preventive role aimed at building long term resilience.
Further, GenAI is capable of automating not only mundane tasks such as data collection, data rationalization, and content summarization but also ones that require a certain degree of critical thinking like data analysis and report generation. Automation of routine tasks frees up human resources for more strategic activities. Let us examine how adopting GenAI can help improve key sub-functions of risk and compliance.
Analyzing regulations to assess their impact on existing controls and define actions that need to be completed to improve compliance processes rely on risk professionals’ ability to parse and analyze information. This is a manual, error-prone, and time-consuming process. With GenAI, the entire process can be orchestrated in a few minutes.
GenAI backed systems can analyze huge amounts of regulatory information, identify changes, and predict impact on business. Using GenAI techniques, BFSI firms can translate these into actionable steps, ensuring compliance with evolving regulations. They can also help review existing policies and procedures, identify gaps, and suggest necessary improvements. GenAI can also be used to summarize regulatory text, information on existing risk controls, and other relevant documents to analyze and extract actionable insights on what improvements are needed to policies and processes that have been impacted. Using the GenAI conversational chat interface, risk professionals can investigate, modify, and curate the output as desired.
With its ability to analyze massive datasets including structured and unstructured information and identify unusual patterns and anomalies, GenAI helps prevent fraudulent transactions from going through. It can also leverage threat intelligence to predict and mitigate potential cyber threats. BFSI firms can up their cybersecurity quotient by using GenAI to generate test scenarios, automate security and controls testing, and provide comprehensive reports on security postures.
GenAI can simulate a range of economic signals to test the resilience of credit, investment, and trading portfolios under different conditions. It can streamline the decision-making processes by assessing risk profiles based on varied micro- and macro-economic factors, generating insightful reports, drafting contracts, and facilitating communication.
GenAI tools can ingest vast amounts of information on a particular topic, transform into a subject matter expert (SME), and provide specialized knowledge and insights. For instance, suppose a loan officer receives a loan application. To approve or reject the loan, the officer must wade through multiple credit policy documents to assess creditworthiness, determine the borrower’s risk profile, and conduct other checks to determine eligibility, and so on. This is a time-consuming process and lengthens the approval cycle, adversely affecting customer experience.
Banks can develop a GenAI tool pre-trained on risk policies, risk assessment, eligibility criteria, and market trends, among others—basically a context-aware SME. The tool can instantly pull up relevant policies and similar loan applications to guide the officer. If the prospective borrower is not eligible for the loan applied for, the tool can even suggest alternative credit products based on the borrower’s profile and prevailing market conditions. This allows the officer to make faster, more informed credit decisions while ensuring compliance with policies and regulations.
With GenAI, BFSI firms can develop sophisticated risk models that incorporate a wide variety of unstructured data sources such as social media, news sites, weather data, among others. Deploying GenAI backed advanced analytics tools can help simulate various scenarios, anticipate potential losses, and suggest strategies to reduce risks. This proactive approach can help banks and insurers better prepare for unforeseen events. In addition, by analyzing real-time and historical data GenAI systems can identify emerging risks and generate early warning signals.
Internal auditors of BFSI firms manually review and analyze contracts, legal documents and internal policies to identify violations. GenAI can understand and interpret legal jargon, identify inconsistencies, and flag potential areas of concern allowing auditors to concentrate on high-risk areas and perform in depth investigations.
A balanced approach to GenAI adoption
While GenAI presents numerous opportunities to improve the risk and compliance function in BFSI firms, it also introduces new risks and comes with several limitations.
Some key challenges and risks of implementing GenAI include:
GenAI requires access to vast amounts of data to learn and generate insights. Ensuring the privacy and security of the data becomes a challenging task when GenAI is adopted at scale. In addition, reliance on third-party data providers amplifies this risk. BFSI firms must implement robust data encryption methods and access controls. Conducting periodic audits and compliance checks can help pinpoint and alleviate potential vulnerabilities. Additionally, partnering with reputable third-party data providers that adhere with strict data protection standards can reduce the risk of data breaches
As GenAI learns from the data it is fed, there is a potential risk of intellectual property theft or misuse. Laying down clear policies and procedures for data usage and intellectual property management complemented with proper training for employees can help. Implementing automated compliance monitoring tools can ensure that GenAI systems operate within legal boundaries.
GenAI outputs are only as unbiased as the data the models are trained on. Regular audits of AI models must be performed to identify and correct biases. Investing in AI literacy programs can educate the workforce on responsible AI use and the importance of ethical considerations in AI development and deployment.
GenAI systems can be complex, posing difficulties in determining how they reach certain conclusions or predictions. BFSI firms must adopt explainable artificial intelligence (XAI) techniques that offer insights into how AI models make decisions. This can help build trust in and transparency of AI systems. Additionally, domain experts must be part of the team that develops and validates AI models which will help ensure the accuracy and reliability of outputs.
GenAI can be compute-intensive and may induce stress on existing infrastructure. Smooth integration of GenAI with existing systems will necessitate thorough assessment of existing infrastructure and identifying potential bottlenecks. Upgrading hardware and optimizing software can help manage the increased computational demands of GenAI. Additionally, integrating GenAI in phases can control disruption and enable slow but steady adaptation.
The GenAI ecosystem is at a nascent stage but BFSI firms are aggressively pushing ahead driven by the fear of falling behind their peers. The adoption of GenAI comes with a range of direct costs such as licensing, model training, system integration, talent management and indirect costs such as operations and maintenance, infrastructure, data security overhaul, and so on.
BFSI firms should define a strategic approach to adopting GenAI while balancing the risks and benefits associated with it. The focus should be on addressing business needs and priorities rather than just technological innovation. We believe that BFSI firms must consider a structured approach that incorporates risk management across the GenAI adoption lifecycle:
- Understand the business context.
- Identify potential use cases.
- Develop a business case that considers all the risks.
- Assess the viability of GenAI solutions with an eye on the costs.
- Establish robust controls and governance mechanisms.
- Run a pilot or a PoC in a controlled environment to identify and mitigate potential financial, ethical, and regulatory risks before embarking on adoption at scale.
Take the next step
GenAI will undoubtedly play a pivotal role in shaping the future of the BFSI sector.
GenAI is poised to be a game-changer, especially in the risk and compliance function. Having said that, firms must prepare for the cultural changes a shift to GenAI will entail while familiarizing themselves with the technology, its capabilities, and shortcomings. Striking a balance between the transformational potential of GenAI and associated risks is crucial to successful adoption. To thrive in the new landscape that GenAI is set to create, risk organizations at BFSI firms must act quickly and proactively, shore up data management, upgrade technology platforms, and identify and address operational impacts.
