Banking on AI: Firms such as BNY balance high risk with the potential for transformative tech

IBM has tuned AI assistants and agents for SAP by getting the LLM to recognise the SAP closed garden. A new challenge for banks and insurers is ensuring that AI agents not only meet their regulatory obligations but also share and reinforce the company’s values and goals. We worried less about the latter when our technology was restricted to the back office and did mostly what it was told. However, generative AI agents are rapidly becoming more powerful, autonomous and ubiquitous, making this an urgent priority. It means banks and insurers that are slow to respond are leaving value on the table, value which their more fleet-footed competitors are seizing as they transform their operating models, their customer experiences and more.

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There are also the technological challenges of enabling coordinated AI agents across increasingly scaled implementations – while ensuring the trust threshold of these agents can be met. Financial services executives will need to acquire a solid understanding of what generative AI agents are, how they work and where they should best be deployed. This will require the rare combination of tech knowledge and deep business process expertise that allows executives, for example, to understand how deficiencies in the firm’s data architecture limit the potential use cases for a particular AI agent. This capability will be invaluable as the pace of change accelerates and the time for decision-making contracts. The AIFinTech100 spotlights technology leaders that are enhancing banking, insurance, and compliance with AI-powered efficiency, personalisation, and intelligent decision-making.

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In recent years, the advent of Generative Artificial Intelligence (Gen AI) has marked a pivotal moment in this journey, introducing capabilities that redefine core banking operations. Once they understand what is expected of them, they will use their capabilities, and communicate and collaborate with other agents in their network, to deliver this outcome. We’re starting to give AI agents real autonomy, and we’re not prepared for what could happen next. Still, it’s seen as a valuable “second pair of eyes” for wealth managers, with potential to evolve into a reliable tool for individual investors too. Its creator, Cognition Labs, calls it ‘the first AI software engineer’ because it can write code, build websites and develop software from simple prompts. The ability of Devin and its counterparts to learn from mistakes and continuously improve makes them far more sophisticated than the AI chatbots of years past.

Within IBM itself, Generao says that AI in security service delivery has saved $8 million in threat operations cost avoidance, and is projected to provide $7.5 million cost savings over three years. Gen AI has enabled automation of over 40% of client requests, while using AI in security testing workflows which has yielded 5% cost savings in XForce Adversary Services Red Team testing for their clients. Fitzgerald highlighted one portfolio company, Stuut, which helps collect accounts receivable through AI, including voice agents.

AI-powered financial literacy programs educate users on budgeting and saving, empowering informed decision-making. The low-cost, cloud-based infrastructure of fintech ensures sustainability, even in remote areas where traditional banks struggle to operate. Platforms such as M-Pesa in Kenya enable rural users to conduct transactions via mobile phones, eliminating the need for physical banks. Micro-lending platforms like Kiva use AI to assess creditworthiness with minimal data, allowing underserved entrepreneurs to access small loans.

All generative AI models potentially suffer from “hallucinations”—where the AI model confidently provides inaccurate outputs—but if those inaccuracies now lead to financial transactions, the consequences could be far more severe. Griffiths says that quality controls like fine-tuning the models and providing them with specific data can help reduce the likelihood of confabulation. Where machine learning would be designed for specific use cases, like fraud modeling or document recognition, large language models can be trained for a wider variety of tasks.

• Innovations like mobile banking, micro-lending, digital wallets, and AI-driven financial literacy programs are key drivers of this change.
• What has become clear is that the capabilities of this innovation are expanding more rapidly than most companies can take advantage of them.
• This not only provides financial autonomy to unbanked populations but also ensures secure, transparent transactions that bypass conventional banking’s stringent documentation requirements.
• Recognize the importance of data privacy and building safeguards to better ensure that customer information is protected.
• Looking ahead, AI-driven predictive models, blockchain, and decentralized finance (DeFi) are set to revolutionize banking.

Not the chosen one, but the brilliant strategist who expands possibilities while also ensuring the system doesn’t collapse under its own weight. Yet, many institutions are treating AI like an all-powerful saviour, rushing to integrate it without a clear blueprint. Some people believe chatbots like ChatGPT can provide an affordable alternative to in-person psychedelic-assisted therapy. It will continue to become an ever-more integral part of everyday life – so consumers need to be proactive to engage with this new and quickly evolving technology. This will involve creating clear guidelines for the development, use and oversight of generative AI systems, balancing innovation with consumer protection.

• By harnessing the power of AI technologies across various facets of financial services, banks can unlock new revenue streams, mitigate risks, and deliver superior value to customers in an increasingly digital and data-driven world.
• So far, 22 are now retro-fitted, which Finch believes has helped make IBM Consulting the fastest growing Gen AI GSI.
• Similarly, in India, the government’s Aadhaar programme, combined with fintech innovations, has successfully banked over 400 million people using biometric IDs.
• Embracing AI can allow you to move beyond legacy systems and build a financial environment that is more responsive, accessible and secure.

While AI has created an arms race across sectors from health care to law firms, banks like BNY still have to proceed with caution. A rogue agent or hallucination could trigger the next financial meltdown, after all, and that’s not to mention the reams of red tape and regulations that institutions have to consider when it comes to sensitive data and customers’ personally identifiable information. Through intelligent document processing and automated reporting, you can meet know-your-customer (KYC) and anti-money-laundering (AML) requirements with greater accuracy and less manual effort. This not only enhances transparency but can also reduce costs and improve internal efficiencies. In assessing creditworthiness, AI allows banks and lenders to go beyond traditional credit scores by evaluating alternative data, such as payment histories and transactional behavior.

The banking industry has undergone a remarkable transformation over the centuries, evolving from rudimentary ledger-based systems to sophisticated digital infrastructures. This evolution has been driven by technological advancements that have continually reshaped how financial services are delivered and consumed. Leah Generao, a Partner with IBM Security Consulting, explained that from an AI perspective, IBM is both improving the productivity of teams with AI as well as managing the security and compliance for clients in AI deployments.

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His dual certifications in AWS and GCP generative AI technologies reflect a rigorous approach to staying at the forefront of the field, while his advisory role with Fortune 500 banks underscores his influence in promoting ethical, scalable AI practices globally. Looking ahead, AI-driven predictive models, blockchain, and decentralized finance (DeFi) are set to revolutionize banking. AI will offer hyper-personalized services, while DeFi could reduce reliance on traditional banks. With global AI investment in banking projected to reach $85 billion by 2030, these innovations promise a more inclusive and accessible financial ecosystem worldwide.

Major fintech companies, such as PayPal and Stripe, prioritize speed, convenience, and security, reshaping consumer expectations. Successful case studies abound, illustrating the transformative impact of these technologies. In Kenya, M-Pesa has integrated over 50 million users into the financial system since its inception in 2007, facilitating payments and savings through simple SMS technology. This platform has not only provided a means for financial transactions but has also stimulated economic activity in regions previously devoid of banking infrastructure. Similarly, in India, the government’s Aadhaar programme, combined with fintech innovations, has successfully banked over 400 million people using biometric IDs.