The European Central Bank states that the impact of AI on the financial sector will depend on how challenges related to data, model development and implementation are addressed. For this reason, some companies are still cautious about (generative) AI. But more and more financial institutions are discovering how AI can be used responsibly.
At Pipple, we have extensive expertise in tackling these challenges. We help companies use AI responsibly, with data-driven solutions that are both explainable and reliable.
How AI will change the financial sector: which processes can be automated?
AI has the power to make the financial sector not only more efficient, but also more reliable and customer-oriented. Financial institutions use AI to better manage risk, streamline operational processes and improve compliance.
One of the most visible applications is automation in customer contact. AI-driven chatbots and virtual assistants offer 24/7 support, reduce the workload on employees and increase customer satisfaction. In addition, AI is frequently used for risk analysis and fraud detection. With the help of Machine Learning models, patterns in financial transactions can be analyzed in real time, enabling faster detection of suspicious activities.
AI is also playing an increasingly important role in the field of regulation and compliance. Financial institutions must comply with increasingly complex laws and regulations. AI helps automate compliance checks, detect anomalies and reduce error-prone manual processes.
At Pipple, we have been working with data and AI in the financial sector since 2016. We are familiar with the challenges that financial institutions face and develop solutions that make an impact. In the next paragraph, we present four concrete AI applications with which we have already delivered demonstrable value to our customers.
4 ways to apply AI in your financial organization
1. Risk management and credit granting processes
The automation of risk management and credit processes with the help of AI can help financial institutions work more efficiently and accurately. An excellent example of this is our collaboration with Argenta. Argenta is one of the ten largest mortgage lenders in the Netherlands and has made significant improvements to their credit risk management with the help of Pipple. This allowed Argenta to discover trends and better manage credit risks in their portfolio. This not only led to significant savings, but also to improved customer interactions. An important development was the improved method for estimating losses more accurately and reserving less money.
2. Automatically handle customer questions and support with an AI chatbot
Handling customer questions and support automatically with an AI chatbot can significantly improve efficiency and customer satisfaction within a financial organization. A striking example of this is the implementation of an AI assistant based on Generative AI by HR service provider Fhris. With a rapidly growing customer base and an increasing number of daily users, the workload of their back office became increasingly heavy. To meet this challenge, Fhris decided to automate the process of handling customer questions with an AI chatbot. We helped them map out a phased rollout, in which the back office staff first get to work with the AI assistant to train the application as they go. Only when the assistant can answer almost every question, will it be made available to end users. The ultimate goal is that users no longer have to submit tickets, but can get a correct and clear answer directly via a search function on the platform. If the chatbot does not know the answer, it will formulate the question in full for the back office, so that they can provide the correct answer in one go. After a few months, the benefits of this approach are already showing: employees can find the right information at the touch of a button, and Fhris is well on its way to halving the number of tickets.
3. Predicting customer behavior with AI
Predicting customer churn with AI offers financial organizations the opportunity to respond proactively to customer behavior and thus improve customer retention. An inspiring example of this is the collaboration with Obvion, where AI was used to identify predictors of customer churn. By predicting which customers are at risk of switching to a competitor, they can prevent this from happening more often, for example through targeted marketing and highly informed advisors. Thanks to the savings from this use case, Obvion had earned back the investment in Pipple within three months.
4. Automation and efficiency improvement
Automation and efficiency improvements are crucial aspects for modern companies, especially in the financial sector where accuracy and speed are essential. An excellent example of this is the story of Achmea Bank. Achmea Bank faced the challenge of keeping up with constantly changing mortgage products, customer compositions and laws and regulations. This requires a large number of models that must be continuously updated. Together, we succeeded in automating their processes and making them more efficient. Achmea Bank can now adapt models to the latest insights at the touch of a button, saving many months of work every year. They report outcomes in a more consistent manner and can calculate much more quickly for auditors and De Nederlandsche Bank (the Dutch central bank) how certain factors influence the model.
These four examples are just a few of the ways AI can be used in financial institutions. We have already helped other clients with predictive models for workload, implementation of IFRS9 and Advanced IRB models, loan monitoring and automation of financial checks. The possibilities are endless.
Would you also like smart AI solutions that really make an impact? Would you like to know how to set up your data strategy or prepare your employees to work with AI? We would be happy to help you. Contact us and discover the possibilities during a free inspiration session!
