Manage compliance and avert regulation-related risk using AI-driven suspicious transaction detection

Between 2022 and 2023 there was an increase of 57% towards AML-related fines imposed on financial institutions amounting to ~$6.6Bn . Money laundering poses a significant threat to financial institutions, resulting in severe legal, financial, and reputational risks. Implementing an advanced anomaly detection and suspicious transaction classification system can enhance the effectiveness of AML efforts, ensuring compliance and safeguarding the institution.

Traditionally, identifying suspicious transactions from billions of records has been done using rule-based approaches. However, this method often leads to false positives and suboptimal thresholds.

Modern AI/ML-driven approaches can process large volumes of data and detect subtle changes that indicate suspicious behavior, improving the accuracy and efficiency of AML efforts.

This solution is a machine learning-based anomaly detection system that utilizes historical transaction data, customer characteristics, and behavioral patterns to identify unusual activities. The system employs supervised learning models to classify transactions as either normal or suspicious, prioritizing those that require manual investigation.

This solution is for organizations that:

1. Have mandates to establish stringent controls around money laundering and handle a high volume of transactions.
2. Already have traditional AML practices in place and seek to enhance these rules using advanced techniques.
3. Aim to develop the most cost-effective team structure for their AML operations.

1. Enhanced Detection Accuracy: Minimizes false positives and enhances the identification of legitimate suspicious transactions.
2. Operational Efficiency: Reduces the workload on compliance teams by automating the initial screening process.
3. Regulatory Compliance: Ensures compliance with AML regulations and mitigates the risk of penalties.
4. Cost Savings: Decreases operational costs related to manual transaction reviews and investigations.

The following data is necessary for this solution:

1. Transaction data
2. Customer-level Profit and Loss (PnL) data
3. Customer device data
4. Labels indicating past anomalies in transactions
5. Existing thresholds, if applicable
6. Regulatory compliance guidelines data

A wide variety of algorithms can be used for these solutions:

1. Supervised: Logistic regression, Support Vector Machines (SVM), Neural Networks, Random Forest
2. Unsupervised: K-means clustering, Autoencoders
3. Ensemble: Boosting and Bagging

1. On prem ( on customer systems or on DeepQ-AI Environment)
2. On hosted cloud space ( Customer or DeepQ-AI Environment)
   ( deployment is subject to data availability in the same environment, or feasibility of seamless data transfers within secured environments)

1.Final outputs could include a set of rules along with their accuracy metrics.
2.Batch files, such as Excel files or flat files.
3.Outputs can be shared through API requests. By sending an API request with inputs, you can receive a response. This can be integrated into an existing application or a separate web app can be created.