The financial services sector faces a turning point in 2025 and beyond Carole Hamilton. And staying in the foreground is not only related to managing credit risks and preventing fraud. Instead, it comes to taking advantage of artificial intelligence, data coordination is better and ending with fragmented decision strategies.
But this means much more than just updating decision systems. Obtaining the risk decision will not come from any isolated reform. Instead, there should be a change in the strategy towards a comprehensive approach to deciding a decision of credit risks and fraud. As for this approach to work, this means alignment with automation of data and decision -making operations to increase the effect.
The interactive approach to risk management will not effectively combat fraud and credit risk management. Simply put, the interactive approach is no longer sufficient. Financial institutions need to adopt a proactive strategy paid from artificial intelligence that integrate risk decisions through the entire life cycle of customers.
A successful approach in the actual time includes the decision of artificial intelligence, with models of artificial intelligence that are constantly learning and adapting to new fraud patterns.
“It is a decisive moment to shift from the very interactive risk management approach to something more intelligent, proactive and dynamic so that the risk of credit is managed dynamically,” says Hamilton.
Hamilton says that fraud and credit risks are often managed in separate silos. The result is the lost shortcomings and visions. The unified decision approach allows a better assessment of risks, faster response times and enhanced customer experiences.
Accordingly, financial institutions need to invest in unified decision -making platforms to eliminate silos, reduce shortcomings and improve the accuracy of risk evaluation.
While financial service providers are increasingly realizing that artificial intelligence can enhance credit risk assessments, enhance detection of fraud and improve operational efficiency, this is only part of the equation. It is true that the adoption of artificial intelligence is accelerating, but the integration of weak data is still a large barrier.
The financial institutions that adopt this shift will be a better position to reduce risk, push growth and provide superior customer experiences.
The challenge facing the sector has been highlighted by a global survey by PROFFENIRS earlier this year.
The main decision makers were erased at the world’s financial services providers to understand the challenges of decision and fraud in the client’s life cycle, the priorities of investment in the decision, and the opportunities for artificial intelligence.
He revealed that approximately half of all executives of financial services are struggling by managing credit risks, discovering and preventing fraud.
The survey also revealed that many of them renew the decision -making strategies in the risks of credit and the prevention of fraud in 2025, where artificial intelligence plays a prominent role.
-
Nearly 60 % say they find it difficult to spread and preserve models of risk decision.
-
55 % of CEOs realize the value of artificial intelligence to make beginner strategic decisions, and in its ability to make recommendations to improve the acting performance.
-
37 % say they are struggling with effective data formatting to prevent application fraud, specifically in the inability to take and integrate new data sources easily.
-
36 % face a challenge in using artificial intelligence and machine learning to prevent fraud.
The main priorities for customer management and account are the actual time decision, which depends on the event (65 %), eliminating friction through the customer life cycle (44 %), and increasing the value of the customer’s life (44 %). More than half of the respondents agree that the biggest data challenge they face is the ability to easily integrate data sources into decision -making operations.
“I would say that the investment certainly happens, and there are many projects that try to get out of the land and start as well. It is implementation, although this is still the challenge. So we see the investment, but I feel that artificial intelligence is still going through a transitional stage of organizations that calculate how they can adopt in their business and make it effective.”
Hamilton suggests that institutions should think about starting a small run and expanding its scope to relieve risks and ensure a measurable effect. This may mean starting artificial intelligence projects that provide a quick return on investment, such as credit registration and customer decision, or perhaps a little less than organized fields such as detection of fraud. The gradual approach, which focuses on early victories, will build confidence in pushed artificial intelligence strategies while showing concrete business values.
“American and Canadian banks lead this charge in adopting artificial intelligence, as it has invested nearly two -thirds of them in artificial intelligence and intelligence that is now higher than any other region. This is a really positive sign, but integration still represents a challenge for banks in North America.
“Interests related to compliance and security, we see higher in Europe, the Middle East and Africa from other regions, as many of them are called as a barrier to relying on artificial intelligence. The challenge facing European banks is that while they are rich in data, they often struggle to organize this together, to cancel the strength of this.
“It is a decisive moment for companies to act, but I think it is a very positive sign of a lot of energy in taking these projects out of the land to unify the decision, bring artificial intelligence and improve data integration.
“The last point is that the discussion often depends on the hypothesis of reducing risks and stopping bad, but we have not truly speaking as much as we could about the ability to open new opportunities for innovation and growth as well as these organizations.
“Because if you really understand who you are dealing with and the threat or risk they are, you will find that where this represents a small threat and a small danger, they may be a great customer for you, and you want to put this time and energy in dealing with it in the right way to pay value to them and your work.”
This is the huge possible challenge and prize. Amnesty International enables pre -emptive participation and designer offers that drive loyalty and increase the customer’s value with artificial intelligence resolution models that guarantee a more focused approach to customers that can adapt dynamically to customer behavior in actual time. Easy to implement, get rid of unnecessary friction while maintaining strong controls for risks.
The banks that can provide more intelligent, faster and more concentrated experiences will be with AI, data and visions in actual time and benefit from excessive customization to increase participation and age value, winners.
“Carole Hamilton from Profinner on the decision of credit risks, and the prevention of fraud and reward” was originally created and published. Banker Retail InternationalThe brand owned by Globaldata.
Information on this site was included in good faith for public media purposes only. It is not intended to reach the advice that you should depend on, and we do not provide any representation, guarantee or guarantee, whether it is explicit or implicit in relation to accurately or completing it. You must have a professional or specialized advice before taking any action based on the content on our website.
