Use of AI in Key Aspects of Customer Management

Challenges of AI in Banking

The true challenges of artificial intelligence (AI) in banking are not technical but operational and regulatory. Since the financial crisis a few years ago, banks have been subject to a stricter regulatory environment, influencing the adoption of new technologies. The need to explain and justify the decisions made by models also imposes restrictions, favoring the use of interpretable models over black-box models. Although advanced AI offers significant benefits, traditional banks must overcome their risk aversion and find adaptability to established procedures to fully adopt it.

Fraud Prediction

Fraud prediction in the banking sector is a complex task that requires a different approach from credit risk evaluation. While credit risk can aggregate transactions to get an overall view of a person's behavior, in the case of fraud, individual transactions are crucial. Additionally, a wider variety of data sources must be considered, including device data and communication history.

Non-Linear Models for Fraud Prediction

The changing nature of fraud requires the rapid implementation of models, and linear models are often not sufficient for this challenge. Therefore, it is common to use non-linear models such as Random Forests and XGBoost, as well as neural networks when incorporating text data is desired. These models require less emphasis on feature engineering, saving time and allowing modeling of behaviors that are not fully understood. However, these models remain predictive and need training data labeled with fraud cases, implying that fraud must occur first.

Unsupervised Methods

To detect potential fraud cases before they occur, unsupervised methods such as clustering algorithms or one-class models like Isolation Forest can also be used. These methods help identify customers with unique behaviors or outliers in general.

Adversarial Machine Learning (AML)

Fraud is not only combated with detection but also with the prevention of attacks on the models themselves. Adversarial Machine Learning focuses on finding and mitigating weak points in the models that attackers could exploit. For example, a poisoning attack involves introducing false data into the training process to corrupt the models unnoticed. In loan automation, fraudsters may try to pose as good customers by lowering their perceived credit risk, either by artificially increasing their income or lying in their applications. Fraud models help cover these deficiencies but must also be evaluated to detect possible adversarial attacks.

The Complexity of Fraud

The diversity and changing nature of fraud require more complex models. In some cases, these models can flag potential frauds for more detailed human review. Fraud models often run alongside credit risk models in automated loan approval systems, complementing each other as they aim to predict default for different reasons.

Customer Retention

AI is also used to predict customer attrition, whether by closing accounts, switching mortgage providers, or reducing their business with the bank. Banks intervene by offering incentives such as lower interest rates or discounts to retain dissatisfied customers. Many of these interventions can be automated. Predictive models help banks anticipate payment defaults and offer solutions such as financial advice or debt restructuring before it occurs.

Customer Service and Chatbots

Automation also improves customer service. AI-powered chatbots can handle repetitive queries quickly, leaving more complex tasks to humans. With advancements in AI, many banks are upgrading their chatbots to use more sophisticated models like LLMs and generative AI. While these models can handle more complex queries and offer personalized advice, they must be used cautiously due to the regulated environment in which banks operate.

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