AI in Financial Services: Decision-Making Applications and Industry Trends

For years, financial institutions have invested heavily in digitising customer interactions. Banks, Fintechs, investment platforms and mobile financial service providers can now reach millions of customers instantly, supported by enormous volumes of transactional and behavioural data.

Yet much of that intelligence is still used for a relatively narrow purpose: predicting who might buy a product, respond to an offer, churn or become inactive.

Enterprise AI company Flytxt believes the next phase will be fundamentally different - with AI moving higher in the financial services value chain, from generating predictions and recommendations to helping institutions formulate strategies, make decisions, orchestrate engagement and continuously optimise business outcomes.

Its recent engagement with IIFL Capital Services is an example of this shift. IIFL is deploying Flytxt's Agentic AI capabilities to deepen investor engagement and unlock greater value from its customer base.

But Flytxt's financial services footprint extends beyond investment services. Its AI is in use across banks including KCB and Zanaco, as well as fintech, insurance companies and mobile financial service providers across diverse markets. Despite their different business models, these institutions face a common challenge - how to spot inefficiencies across their marketplace operations and overcome them quickly with right strategies and decisions.

“AI will create its greatest value in financial services when it moves from supporting individual tasks to improving the quality of business decisions,” says Dr. Vinod Vasudevan, CEO, Flytxt. “The opportunity is to connect business objectives with strategies, decisions and actions, and continuously optimise them based on outcomes. That is when AI becomes part of how the enterprise operates, rather than simply another technology layer.”

From Prediction to Decisioning

Traditional analytics can predict that a customer may churn, purchase a product or respond to an offer. But the institution still needs to decide which opportunity matters, what strategy should be pursued and which engagement will produce the desired outcome.

Flytxt combines Agentic AI with causal intelligence to address this gap. While conventional predictive models largely identify correlations from historical data, causal intelligence seeks to understand why outcomes occur and which decisions are most likely to influence them.

For a bank, this could mean determining the right strategies to increase CASA balance, improve retention or drive credit growth. An investment services company may seek to activate dormant investors and grow Assets under Management.

Optimising Financial Services Marketplaces

Flytxt sees the larger opportunity as optimisation of financial services marketplaces - complex environments where millions of customers with different needs interact with multiple products, channels and business objectives.

This requires more than personalisation. AI needs to balance customer context and intent with enterprise objectives, while operating within boundaries around privacy, explainability, governance, risk and regulatory compliance.

The IIFL Capital engagement brings this proposition into India's rapidly evolving investment services market, where the challenge has increasingly shifted from simply acquiring digital investors to sustaining engagement and growing relationships over time.

Flytxt's financial services expansion builds on more than 15 years of applying AI to complex customer marketplaces, initially in telecom. The company is now extending that experience across banking, fintech, mobile financial services and investment services.

The broader transformation it sees is from digital enterprises to intelligent enterprises.

Digitisation gave financial institutions data, channels and automation. The next phase of AI could connect them into a continuously learning intelligence layer - one that understands objectives, helps formulate strategies, makes decisions, orchestrates engagement and measures whether they delivered the intended outcomes.

As financial products and digital interfaces become increasingly comparable, competitive advantage may increasingly come down to which institution makes the best decisions with its intelligence and consistently converts those decisions into measurable outcomes.

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