AI’s Role in Fighting Fraud for Bank Payments

Transaction classification for bank card payments is a complex task for several reasons. Firstly, there may be a large volume of transactions to classify, making it difficult to manually review and categorise them all. Additionally, the classification process may require a deep understanding of the merchant and the type of goods or services being purchased, which can be difficult to determine from the limited information available in a bank transaction. Furthermore, the number of merchants and types of goods and services that a bank's customers may purchase from are constantly changing, making it difficult to maintain an up-to-date list of merchants and categories. Additionally, the transaction classification may be sensitive to fraud or money laundering, which can add complexity to the process.

The purpose of transaction classification for bank card payments is to understand the spending habits of customers and to identify any suspicious or unusual activity. This information can be used to detect and prevent fraud, money laundering, and other illegal activities. Additionally, the classification of transactions can be used to generate financial reports and to create targeted marketing campaigns for the bank's customers.

An AI-based classification approach can be used to improve the accuracy and efficiency of transaction classification. The approach includes the following steps:

1. Enrich Data with keywords based on Azure Cognitive Bing Search API fed with transaction details. This will provide additional context and information about the merchants and products involved in the transactions. The technical approach in this step could include using natural language processing (NLP) techniques to extract relevant information from the transaction details and feed it into the Bing Search API. This step could also include data pre-processing, cleaning and normalization. Challenges in this step could include dealing with unstructured data, missing or incomplete information and data privacy concerns.
2. A multilingual model is trained to tag products based on the bank's metadata + Bing Keywords. This model will be able to classify transactions in multiple languages, and will be able to handle the ever-changing list of merchants and products. The technical approach in this step could include using machine learning techniques such as supervised or unsupervised learning, depending on the availability of labeled data. The model could be trained on a dataset of transactions that have been labeled with merchant and product categories. The challenges in this step could include dealing with imbalanced datasets, the need for large amounts of labeled data, and the need for a deep understanding of the merchant and product taxonomy.
3. Add Active Learning to the pipeline, with an engineer manually correcting the lowest confidence predictions, and the model is updated. The active learning process will allow the model to improve over time as it receives feedback on its predictions. The technical approach in this step could include using active learning techniques such as uncertainty sampling or query-by-committee to select the transactions that the engineer should review. Challenges in this step could include ensuring the quality of the labels provided by the engineer, the need for a large number of labeled transactions to improve the model, and the need for an efficient interface for the engineer to review and label the transactions.
4. Evaluate the final model. The final model can be deployed, and will be monitored against the same evaluation set. The evaluation will provide insight into the model's performance and areas for improvement. The technical approach in this step could include using evaluation metrics such as precision, recall, F1-score and confusion matrix to evaluate the model's performance. The challenges in this step could include the need for a large and representative evaluation set, the need for an efficient way to monitor the model's performance in production, and the need to adapt the model to changing business needs.

Note: The above steps are a general approach, the exact implementation could change based on the bank's infrastructure and specific use case.

AI can be used for anti-money laundering (AML) and fraud detection within the domain of transaction classification for banks in several ways:

• ‍Anomaly detection:
  AI algorithms such as unsupervised machine learning techniques can be used to identify patterns in transaction data that deviate from normal behaviour. These patterns could indicate money laundering or fraud activities.‍
• Link analysis:
  AI algorithms can be used to identify connections between different transactions, accounts, and individuals. These connections can help to identify money laundering networks and fraudulent schemes.‍
• Risk scoring:
  AI algorithms can be used to assign risk scores to transactions, accounts, and individuals based on their behaviour patterns and connections. These scores can be used to prioritise which transactions or accounts to investigate further.
• Natural Language Processing (NLP):
  AI algorithms can be used to extract relevant information from transaction descriptions, customer names, and other unstructured data. This information can be used to identify suspicious transactions or to flag transactions for further review.‍
• Robustness and scalability:
  AI algorithms can handle large volumes of data and can be easily scaled up to match the growing volume of transactions.‍
• Real-time monitoring:
  AI algorithms can be used to monitor transactions in real-time, which allows banks to detect and respond to suspicious activities quickly.

It's worth noting that AI-based approaches for AML and fraud detection can complement the traditional rule-based systems and human review, which are commonly used in banks. AI can help to identify patterns and connections that might be missed by human analysts and can help to reduce the number of false positives that need to be reviewed by human analysts. Additionally, the use of AI-based approaches can help the bank to comply with the regulatory requirements around AML and fraud detection.