Varun Resh
Fintech & Emerging Technologies
Fintech and artificial intelligence (AI) have been close allies for years, with AI already making its mark in various aspects of the financial services industry. According to Precedence Research, the global Generative AI in banking and finance market size was estimated at $712.4 million in 2022 and it is expected to reach around $12,337.87 million by 2032, poised to grow at a CAGR of 33% during the forecast period from 2023 to 2032.
While AI has already found its place in the operations of 90% of fintech companies, the financial services sector is undergoing a significant technological revolution. Both traditional financial institutions and fintechs are now shifting their focus from AI-driven cost reduction to leveraging its capabilities for revenue generation. This shift brings forth distinct AI strategies, where traditional institutions improve existing products and services by partnering with fintechs, while fintechs drive innovation and digital transformation through AI.
Generative AI's Emergence and Potential
A new game-changer has emerged in the form of generative AI, holding the promise to revolutionise the financial services landscape. However, we are merely scratching the surface of its potential, and there is a long journey ahead before generative AI reaches its full capabilities.
AI applications can quickly learn from data, analyse new information from various sources, and adapt in real time. This accuracy is incredibly valuable to businesses, as it improves customer experience and increases efficiency by transforming how we approach daily tasks. Nevertheless, the adoption of this transformative technology also presents new challenges, amplifies existing risks and latest opportunities for bank-fintech partnerships. This has sparked an ongoing debate surrounding regulation, aiming to ensure that generative AI serves the best interests of all stakeholders involved for product innovation and faster go-to-market strategy.
Addressing Data Protection Implications: To fully comprehend the implications of generative AI on the financial sector, it is crucial to examine its data protection implications and how existing legal requirements and guidance apply to this technology. Safeguarding consumers, firms, and the stability of the financial system becomes paramount for financial institutions as they manage and mitigate the risks and potential harms associated with the use of generative AI. In this context, consumer protection and data protection are of significant concern, as highlighted by the Financial Conduct Authority (FCA). High-quality data plays a fundamental role in safe and responsible generative AI adoption within the UK financial services, encompassing data sourcing, creation, training, testing, validation, and continuous analysis. Consequently, data security, data protection, and individuals’ privacy are more important than ever to ensure the safe and responsible adoption of generative AI.
Balancing Benefits and Risks: The benefits and risks of Generative AI in financial services manifest at various levels within the system, encompassing data, models, and governance. Addressing the risk drivers can unlock the potential benefits, such as highly accurate outputs. However, the advantages and risks of generative AI hinge on the specific context and purpose of its use, particularly in areas like consumer protection, safety and soundness, and financial stability. Here the digital sandbox comes into the rescue, as it supports to evaluate and scale third-party Gen AI technologies into production.
”The Economist Intelligence Unit, 77% of bankers believe that the ability to unlock the value of AI will be the difference between the success or failure of banks. Gen AI could generate 2.8 to 4.7% of the Industry’s revenue from increased productivity.” Source – McKinsey & Company Report
Product innovation and usecases:
Fraud detection in Transactions and Payments – Detect anomalies in data by modelling normal patterns and identifying deviations, to detect fraud, cybersecurity threats, or quality control. It Provides an interactive and contextualised experience between customers and financial institutions. The ability of generative AI to ingest vast amounts of data for different usecases, such as payments, ESG, SME/SMB, Digital Assets, enables it to engage in conversational interactions, transforming the way customers interact with financial institutions.
Algorithmic trading – Leveraging AI algorithms to improve accuracy and profitability over time by continuously analysing market data and adjusting trading strategies based on historical performance to increase speed and result in profitability.
Conversational AI – Power voice assistants and IVR systems, enabling customers to interact with automated systems using natural language speech. Generate automated responses to customer inquiries, such as frequently asked questions or routine support requests. Generative AI models can provide timely and accurate responses, freeing up customer service agents to handle more complex or specialised issues.
Compliance – Improve due diligence reviews: Large volumes of documentation (statements, activity reports, filings, emails, contracts) can be analysed continuously to ensure ongoing adherence to regulation and reduce the need for deeper reviews.
Data access: Compliance officers often struggle to piece together fragmented data from multiple sources, hindering their ability to make informed decisions on flagged transactions or customers. Enhanced LLMs can swiftly reduce research time and provide synthesised information, offering a complete view of risk exposure and streamlining risk management.
How Can Financial Institutions Leverage AI by Partnering with fintechs?
AI has become a widespread technology within the financial services industry. Major organisations like JPMorgan Chase have invested hundreds of millions of dollars in AI and have seen significant returns on their investments. In fact, JPMorgan Chase spent $12 billion on technology in 2021 alone. AI adoption is not limited to large institutions, as a 2022 report by NVIDIA revealed that over 75% of companies in various financial sectors, including capital markets, investment banking, retail banking, and fintech, utilise AI in the form of machine learning, deep learning, and high-performance computing. This indicates the pervasive nature of AI within the industry. (source: baincapitalventures)
Governance: Accessing AI including LLM’s and apps is controlled and auditable through the off-estate Sandbox Environment.
Security Constraint: Third-party off estate environments enable secure and controlled access to technology that is restricted within the bank’s environment.
Data Sharing: With NayaOne you can leverage statistically accurate synthetic datasets to evaluate AI without leaking sensitive information.
Accuracy & Validity: Opportunity and access to multi-vendor comparisons using the same dataset to compare the output without leaking sensitive data, onboarding the vendor or accessing it via the bank.
Fit to business need: Banks can validate multiple vendors off estate against the same KPI’s as well as deploy and train LLM’s within an air gapped environment.
Scalability: Scalability and stress testing of the application can be evaluated iteratively off estate by increasing the data input. Use Case scalability can be evaluated through repeated experiments varying the data type and theme of questioning.
Generative AI has the potential to unleash transformative power within the financial sector. It can revolutionise the delivery of financial advice, enhance fraud detection and AML efforts, and enable financial institutions to modernise their infrastructure. However, the journey towards realising its full potential requires managing risks, addressing data protection concerns, and ensuring safe and responsible adoption. By navigating these challenges, the financial sector can harness the benefits of generative AI while safeguarding the interests of all stakeholders involved, ultimately driving innovation and shaping the future of financial services.
