From fraud management to customer relationship management: An AI revolution

“Nobody is ahead of anybody else by more than two to six months.” This quote from Yann LeCun, Chief AI Scientist at Meta was presented during the session and captured the overall sentiment very well. All panelists agreed that we are still in the early stages of the adoption of AI, and it will be exciting to see where this journey will lead us. Companies should not be scared off by the innovation since there is plenty of time to hop on the wagon. But when they do, what should be taken into consideration?

During the expert session ‘Beyond the buzzwords: Understanding the reality of AI’s reach’ the potential of AI in the financial world was discussed. Several use cases were discussed, ranging from fraud prevention, assessment of risks, business models and revenue streams to hyper personalization. In this blog I will zoom in a bit more on some of these use cases.

How is AI driving CRM innovation?

The marriage of AI and Customer Relationship Management (CRM) is reshaping how financial institutions engage with customers, tailor their services, and enhance overall user experience. Traditional CRM systems relied heavily on manual data entry and analysis, leading to inefficiencies and rigidity. By comparison, AI integration facilitates the automation of various processes and provides insightful data-driven recommendations.

AI can process significant customer data in real-time instead of manual systems and human-led inputs. This enables financial institutions to gain deeper insights into customer behaviour, preferences, and needs, allowing them to anticipate customer requirements and offer tailored solutions. As a result, interactions can be made more meaningful and relevant, placing the customer experience at the forefront.

The importance of trust

The financial services and payments industry relies on trust, and AI has huge potential for fraud prevention and authentication. During the session, panellists discussed the difference between ‘traditional’ AI and generative AI. The first, we know more or less how to control but with the latter we are still in the early stages. Since trust in financial institutions is crucial, banks should be cautious with generative AI. Exploring AI is interesting for banks and something they should be comfortable doing. However, clients are not too keen on their bank doing something risky. It’s a fine line between the two and it is key to find the balance.

With AI we can take personalisation to the next level to increase trust, meaning that brand loyalty can be increased. Therefore, understanding customer behaviours and preferences is key to creating the right services and products for them. AI-powered CRM systems use historical data and predictive analytics to understand the customer, supporting the creation of highly personalised offerings and experiences.

Risk assessment and fraud detection

AI's capabilities extend beyond personalisation. According to one of the panelists, using AI for risk assessment and fraud detection is probably one of the most prominent use cases. In the finance and payments industry, security is paramount for banks and consumers. AI algorithms can analyse vast amounts of transaction data to detect unusual patterns that might indicate fraudulent activities. This proactive ability enables financial institutions to promptly identify suspicious transactions and mitigate potential risks by taking preventive measures well ahead of time.

In addition to fraud and security applications, AI can be valuable when evaluating creditworthiness. By analysing an applicant's financial history, credit score, and other relevant factors, AI streamlines the lending process and supports fair and informed decision-making by banks.

Predictive insights and a more compelling experience

AI's biggest potential lies in its predictive capabilities. Financial institutions can more accurately forecast market trends and customer behaviours accurately, helping them keep their finger on the pulse of market trends. Through analysis of market trends and customer data, AI-powered CRM systems can provide insights into potential market shifts, and help businesses better understand their customer's profiles. From this, AI can aid in identifying upselling and cross-selling opportunities, leading to increased revenue and a more personalised customer experience. During the expert session some fascinating examples were raised, including letting AI use satellite data about water patterns to increase crop yield. Maybe the biggest potential for AI lies in letting it have a crack at some of the biggest problems we as humans cannot solve.

Striking the balance between innovation and ethical practice

AI undoubtedly offers the financial world myriad benefits. However, with it come multiple considerations. Key among these is the responsible handling of customer data. Financial institutions must ensure that data privacy and security regulations are strictly adhered to, and customers' personal information is safeguarded. During the session, the audience was asked what they are most worried about regarding AI and approximately 70% of the responses were risk & governance.

Industry players should also consider that it's difficult to duplicate the human touch despite AI offering a deeper, more personalised customer experience. Whilst routine tasks can be made more efficient, and back-end services can be expanded, payment providers and financial institutions should remember that human interactions remain invaluable, especially in a rapidly digitising world.

The application of AI in the financial services and payments industry marks a transformative era. From personalised customer experiences and risk assessment to predictive insights and ethical considerations, AI is reshaping the landscape. As technology advances, financial institutions embracing AI stand to gain a competitive edge by delivering exceptional service and building lasting customer relationships in this dynamic sector.