AI and Reinforcement Learning in Banking: exploring the Epsilon-Greedy Algorithm for Credit Card Incentives

Artificial Intelligence (AI) is reshaping the banking sector in unprecedented ways. Within the varied repertoire of AI methods, reinforcement learning (RL) stands as a pivotal technique for decision-making. One notable RL strategy, the epsilon-greedy algorithm, offers a unique approach to optimizing credit card incentive programs.

There are many areas in banking where RL methods like epsilon-greedy could be applied:

• Customer Segmentation: Personalizing banking services for different customer segments.
• Fraud Detection: Deciding whether a transaction is legitimate or not.
• Risk Management: Portfolio optimization, loan approval, and other risk-related decisions.
• Customer Engagement: Deciding which offers or incentives might be most appealing to customers to maximize engagement or profits.
• Automated Trading: For institutions engaged in trading activities.

However, the implementation of RL algorithms in the financial sector often faces issues like regulatory constraints, data privacy concerns, and the need for interpretable models, which might limit their practical application.

The epsilon-greedy algorithm aims to find the optimal decision over a series of steps by balancing "exploration" and "exploitation." (very important concepts in machine learning)

- Initialization: At the onset, each possible action has an initial estimated reward. As actions are taken, these estimates are updated.

- Action Selection: With probability (1 - "epsilon"), the algorithm chooses the action with the highest estimated reward, known as "exploitation." With probability "epsilon", it opts for a random action, known as "exploration."

- Update: Following each action, its corresponding reward is observed, and estimates are updated.

- Epsilon Decay: Over time, "epsilon" decays, enabling the algorithm to increasingly favor "exploitation" over "exploration."

The Fisherman's Dilemma: Hook, Line, and Epsilon!

Now let´s translate in a simple metaphor: imagine the epsilon-greedy algorithm as a seasoned fisherman who wants to maximize his catch. He has a lake full of fish but doesn't know where the most fish are located. He must choose between casting his net in familiar, productive spots ("exploitation") and exploring new, untested waters ("exploration").

Initialization: When he starts fishing, he has only guesses about which spots are likely to be most rewarding. Maybe some places look promising because of underwater structures, water clarity, or other signs. These are his initial "estimated rewards" for each possible fishing spot.

Action Selection: each time he casts his net, he faces a decision. He can rely on his past experience and choose a spot where he's caught many fish before, exploiting his current knowledge. Or he can try a new spot, exploring the unknown. The likelihood of him choosing exploitation over exploration is determined by a variable—let's call it his "curiosity level" but in ML we refer to it as "epsilon".

• With a high probability (1 - epsilon), he chooses the most promising spot based on his past experiences ("exploitation").
• With a smaller probability (epsilon), he tries a completely random spot ("exploration").

Update: after each cast, he counts the number of fish he's caught. This new information updates his beliefs about how rewarding each spot is. If a previously untested spot yields a lot of fish, that location becomes a new "high-reward" area for future consideration.

Epsilon Decay: as the day progresses, our fisherman becomes more confident about where the fish are most abundant. His "curiosity level" or "epsilon" decreases. He's less inclined to explore new areas and more likely to exploit the best spots he's discovered.

By the end of the day, he has efficiently balanced exploration and exploitation to maximize his total catch, just like the epsilon-greedy algorithm aims to maximize rewards over a series of decisions.

Application Scenario: Credit Card Incentive Programs

- Exploitation: This might mean focusing on incentives in categories where spending is already high to maintain or increase the share of wallet.

- Exploration: Occasionally, incentives in less popular or new categories are offered to stir spending behaviors, thus providing valuable insights into customer preferences.

The use of epsilon-greedy algorithms within an RL framework could result in more dynamic decision-making processes. They could guide strategies across departments, from risk assessment to customer engagement, optimizing decisions based on real-time feedback.

The epsilon-greedy algorithm stands as an exemplary model for modern banking decision-making. As part of the wider application of AI and RL in the banking sector, this method can significantly impact how financial institutions engage with customers through credit card incentives, offering a data-backed approach to customer engagement and loyalty.