FinTech companies race to turn AI adoption into measurable value, says report

By Divya Shah

In the financial services industry, artificial intelligence (AI) has progressed beyond testing. According to the report Beyond the AI Pilot: Scaling Value in BFSI by Alvarez & Marsal and Beams Fintech Fund, the conversation has shifted from whether AI works to whether it can generate sustainable and measurable business value at scale.

For FinTech, this marks a crucial turning point as the report finds that AI is already being deployed across customer acquisition, onboarding, underwriting, servicing, collections, compliance, and product development. Operational benefits are increasingly visible, but institutions are still grappling with how to convert those gains into meaningful profit-and-loss outcomes. 

The report identifies six major areas where AI is reshaping BFSI: 

• Customer acquisition  

• Onboarding and documentation 

• Underwriting and risk assessment 

• Servicing and engagement 

• Collections and compliance, and  

• Customer growth and product creation.  

Across these functions, AI is helping firms automate routine tasks, improve decision-making, and personalise customer interactions.   

For FinTech companies, customer-facing applications are among the most mature use cases. AI is enabling hyper-personalised customer acquisition by analysing intent signals, behavioural patterns, and contextual data to improve targeting and conversion. Rather than relying on broad customer segments, institutions are increasingly using AI to determine the right product, for the right customer, at the right moment. 

The payments and digital banking segments provide some of the strongest examples highlighted in the report. Niyo increased AI-handled customer support interactions from approximately 10% to 90% while keeping support headcount flat despite a fourfold increase in transacting customers. The company is also extending AI into travel-finance journeys and customer engagement workflows.  

In lending, AI is helping FinTech improve underwriting efficiency and credit assessment. The report cites examples such as Kissht, which achieved a 30% improvement in first-time-right rates and a 2.5 percentage point improvement in credit decisioning performance. Meanwhile, Tata Capital reported a roughly 30% increase in underwriting productivity through AI-enabled processes.   

However, the report’s central argument is that operational improvements do not automatically translate into financial outcomes. AI deployment sits at the beginning of the value chain. Institutions must still prove that improved productivity leads to better business performance and ultimately to banked value reflected in profitability. The report describes this as the gap between operational impact and attributable economic value. 

Sushil Zaregaonkar, Managing Director, Business Transformation Services, Alvarez & Marsal, said: “The question for financial institutions is no longer whether AI can improve an individual task. It is whether they can redesign the workflow, operating model and governance around that capability to capture the benefit. Institutions that treat AI as a layer added to existing processes may see productivity gains; those that redesign how work gets done have a greater opportunity to translate those gains into structural advantage.”

 A major challenge is that AI rarely operates independently in regulated financial environments. Human oversight remains essential in high-stakes functions such as underwriting, fraud management, and compliance. As a result, AI is increasingly augmenting human decision-making rather than replacing it entirely.   

The report also identifies four major barriers preventing FinTech and financial institutions from scaling AI successfully: fragmented data, workflow redesign requirements, governance and deployment complexities, and talent shortages.  

Many institutions possess valuable customer and transaction data, but it remains scattered across systems, limiting AI’s effectiveness. Similarly, legacy workflows often need to be redesigned before AI can deliver meaningful productivity gains. 

Perhaps the report’s most important finding is that the competitive advantage in AI will not come from access to models alone. As AI technologies become more widely available, differentiation will increasingly depend on how effectively FinTech integrate AI into proprietary workflows, customer journeys and decision-making processes.   

For FinTech firms, the next phase of AI adoption will therefore be defined by execution rather than experimentation. The winners are unlikely to be those launching the most pilots, but those capable of translating AI-driven operational improvements into measurable revenue growth, lower costs, stronger risk outcomes and sustainable returns.