How are financial services providers leveraging AI today?

Fintechs

Fintechs, have historically faced significant challenges in competing with banks and other established financial service providers. These fintechs typically have less capital than banks and offer newer, unproven products while still striving to become profitable.

However, their agility, absence of legacy code and bureaucracy, relatively less regulatory constraints, and a digital-first approach to financial services offer them substantial benefits.

In the realm of AI integration, fintechs’ primary advantage has been their speed in adopting and introducing new features, aiming to draw customers from traditional providers. Early applications of AI by fintechs have largely been directed towards external product development and expansion. They have developed customer-oriented features such as interactive apps to supersede conventional chatbots, accounting assistants, expense tracking tools, and algorithm-driven investment tools. These are among the initial instances of fintechs’ efforts to attract customers by integrating AI into their existing offerings.

Banks

Compared to fintechs, traditional financial institutions possess a unique set of strengths and obstacles. They enjoy a significantly larger scale; for instance, Nubank, the world’s largest neobank, holds around $10 billion in customer assets, while JP Morgan, the world’s largest consumer bank, manages approximately $3 trillion in assets. Banks also gain from the low capital cost due to their access to almost free customer deposits, their status as licensed financial institutions with a well-defined regulatory framework, their brand trust and franchise value, and their extensive workforce.

However, some of these benefits come with their own set of challenges: a well-defined regulatory framework for banks also means higher compliance costs and overhead, larger penalties for non-compliance, and more restricted product innovation. The size and scale of bank workforces introduce bureaucracy, hinder agile decision-making, and create costs that increase linearly with customers and assets.

Banks have concentrated their initial efforts in Gen AI on different needs than fintechs. Instead of focusing primarily on customer-facing products, banks are using machine learning and Gen AI to enhance the efficiency of their back-office processes, reduce costs, automate previously manual tasks, improve regulatory compliance, and minimize the risks associated with serving such a large customer base. Interestingly, while some of the back-office and CFO suite Gen AI applications that banks are using are developed internally, many are purchased from fintechs that specialize in creating superior backend solutions for institutions.

This focus on risk mitigation and cost reduction has resulted in investments in products like AI-driven KYC checks, algorithmic underwriting, Level 1 customer management voice and chatbots, portfolio monitoring and fraud prevention tools, and automated compliance management platforms.

B2B fintechs aiming to sell their products to banks have taken note of this focus, as demonstrated by platforms like Hyperplane and Casca. Hyperplane concentrates on consolidating customer information across fragmented bank databases in a compliant manner, assisting banks in investing in more customized products. Casca offers a loan management-specific conversational AI to aid existing bank lending teams in gathering information and processing loan applications more efficiently and compliantly.