The financial services sector is facing an inflection point in 2025 and beyond says Carol Hamilton. And staying ahead is not just about managing credit risk and preventing fraud. Instead, it is about leveraging AI, better data orchestration and an end to fragmented decisioning strategies.
Douglas Blakey speaks with Carol Hamilton, Chief Product Officer at Provenir
But it means a lot more than just modernising decisioning systems. Getting risk decisioning right will not come from any isolated fix. Instead, there needs to be a change of strategy towards a holistic approach to credit risk decisioning and fraud prevention. And for that approach to work it means aligning data automation and decisioning processes to maximise impact.
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From reactive to proactive
A reactive approach to risk management will not effectively combat fraud and manage credit risk. Put simply, a reactive approach is no longer enough. Financial institutions need to embrace a proactive, AI driven strategy that integrates risk decisioning across the entire customer life cycle.
A successful approach includes real time, AI power decisioning, with AI driven models that continuously learn and adapt to new fraud patterns.
“It is a critical moment for a shift from a very reactive risk management approach to something much more intelligence driven, proactive and dynamic so that that credit risk is managed dynamically,” says Hamilton.
Eliminate silos, end poor data integration
Fraud and credit risk are often managed in separate silos, says Hamilton. The result is inefficiencies and missed insights. A unified decisioning approach enables better risk assessment, faster response times and enhanced customer experiences.
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By GlobalDataAccordingly, financial institutions need to invest in unified decisioning platforms to eliminate silos, reduce inefficiencies and improve risk assessment accuracy.
While financial service providers increasingly recognise that AI can enhance credit risk assessments, strengthen fraud detection and improve operational efficiency, that is only part of the equation. It is true that AI adoption is accelerating but poor data integration remains a significant barrier.
The financial institutions that embrace this transformation will be better positioned to mitigate risks, drive growth and deliver superior customer experiences.
The extent of the challenge facing the sector was highlighted by a global survey conducted by Provenir earlier this year.
Key decision makers at financial services providers globally were surveyed to understand their risk decisioning and fraud challenges across the customer lifecycle, decisioning investment priorities, and AI opportunities.
It revealed that nearly half of all financial services executives are struggling with managing credit risk and detecting and preventing fraud.
The survey also disclosed that many are revamping their credit risk decisioning and fraud prevention strategies in 2025, with AI playing a prominent role.
Key takeaways included:
- Nearly 60% say they find it difficult to deploy and maintain risk decisioning models.
- 55% of executives recognise the value of AI to make streamlined strategy decisions, and in its ability to provide AI-powered performance improvement recommendations.
- 37% say they struggle with effective data orchestration for application fraud prevention, specifically in not being able to easily ingest and integrate new data sources.
- 36% are challenged in using AI and machine learning for fraud prevention.
Key priorities for customer and account management are real-time, event-driven decisioning (65%), eliminating friction across the customer lifecycle (44%), and increasing customer lifetime value (44%). Over half of respondents agree the biggest data challenge they face is being able to easily integrate data sources into decisioning processes.
Execution remains the challenge
“I would say that investment is definitely happening, and there are many more projects that are trying to get off the ground and start as well. It is the execution, though that remains the challenge. So we are seeing the investment, but I feel that AI is still going through a transition of organisations figuring that how they can adopt it in their business and make it effective.”
Hamilton suggests that organisations should consider starting small and scaling smartly to mitigate risk and ensure measurable impact. That would mean starting with AI projects that offer a quick return on investment, such as credit scoring and automated customer decisioning, or maybe slightly less regulated areas such as fraud detection. A phased approach, focused on early wins, will build confidence in AI driven strategies while demonstrating tangible business values.
“US and Canadian banks are leading the charge in AI adoption, with nearly two thirds of them investing in AI and embedded intelligence now, higher than any other region. So that’s a really positive sign, but integration does remain a challenge for the North American banks.
“Compliance and security concerns we do see higher in EMEA than other regions, with many of them calling that out as a barrier to AI adoption. The challenge for European banks is that while they’re data rich they often struggle to orchestrate that together, to unlock the power of it.
“It is a critical moment for companies to act but I do think that it’s a very positive sign that there’s so much energy going into getting these projects off the ground to unify the decisioning, bring in AI and optimise data integration.
Unlocking new opportunities for innovation and growth
“The final point is that the discussion is often based on the premise of reducing risk and stopping the bad but we haven’t really talked half as much as we could around the power to actually unlock new opportunities for innovation and growth as well for these organisations.
“Because if you really understand who you’re doing business with and the threat or risk that they pose, you will find that where that’s a small threat and a small risk, they could be a fantastic customer for you, that you want to put that time and energy into engaging with in the right way to drive value for them and your business.”
That then is the challenge and the huge potential prize. AI enabling proactive engagement and tailored offers that drive loyalty and maximise customer value with AI powered decisioning models ensuring a more customer centric approach which can adapt dynamically to customer behaviour in real time. Eliminating unnecessary friction while maintaining strong risk controls is easy to summarise-harder to execute.
Banks that can deliver smarter, faster and more customer centric experiences with AI and real time data and insights and leverage hyper personalisation to increase engagement and lifetime value, will be the winners.
