Building a fraud prediction model using machine learning can significantly enhance a bank’s ability to detect anomalous transactions, safeguarding both the institution and its customers from potential financial losses.
To build a fraud prediction model, the institutions need to follow logical steps considering the objectives and goals that the institution wants to achieve in fraud detection, prevention, and risk management.
Building a Fraud Prediction Model
Below are the steps that an institution may follow to build a custom fraud prediction model:
Step 1: Define project goals and measurement metrics
The first step for any project of data science the project goals may include answers to the following broader questions:
What are the fraud cases that we wanted to identify?
What kind of analytics techniques have already been implemented to combat fraud?
What are the key measurement metrics that we wanted to focus on when assessing the effectiveness of our fraud detection system?
What and how many developers do we need for developing the fraud detection system?
Step 2: Identify proper data sources
Once the business objectives have been confirmed and communicated, we start to identify and collect proper data sources for the fraud detection system. The common data sources for detecting fraud include:
client profile
risk profile
product usage
billing data
Additional data could also be available from third-party data vendors. For example, for the financial services industry, we will incorporate government compliance data when building the fraud model.
Step 3: Design the fraud detection system architecture
Multiple key factors needed to be considered when designing the fraud detection system architecture.
Detection frequency determines how often we run the new data through our fraud scoring model.
Fraud-prevention operation flow impacts how and when we flag different events as suspicious, and how to handle and confirm those suspicious cases afterward.
The scoring accuracy baseline helps us to assess the qualification of our fraud scoring model.
Step 4: Develop the data engineering, transformation, and modeling pipelines
After we have envisioned the architecture of the fraud detection solution, we will start the development of the data engineering, transformation, and modeling pipelines. I have listed key activities for each of those pipelines in the graph below.
For the data engineering pipeline, we need to ingest and merge the data from different sources, aggregate the data based on business metrics, and set up batch processes.
For the data transformation pipeline, the main goal is to improve the data quality, deal with data issues such as missing & incorrect data, and convert the data so that it could be fed into machine learning models.
For the machine learning model pipeline, we focus on building and comparing diversified ML models based on key business metrics. A module for automated model accuracy testing and re-training is a necessity in the production environment to avoid model drifting issues.
Step 5: Integrate the model into the case management system
The final step is to incorporate our best-performing ML model into the case management system. We can rank the risk level of individual cases based on the risk score that we generated. Then, a list of highly suspicious cases will be sent and assigned to relationship managers for further review through the case management system.
Final Thoughts
In building a custom fraud prediction model, institutions should methodically outline their objectives, from recognizing the specific fraud cases to targeting to gauging the system’s efficacy. This starts with defining the project goals, which provide the framework for all ensuing steps. Then, collecting accurate data from both internal and third-party sources becomes paramount, ensuring the system has a comprehensive dataset to learn from.
The architectural design of the fraud detection system then determines how frequently the data is checked and how suspicious cases are flagged and subsequently managed. Central to this process is the development of data engineering, transformation, and machine learning pipelines, ensuring data quality and the effectiveness of predictive algorithms. Finally, seamlessly integrating this model into the existing case management system ensures high-risk cases are promptly identified and reviewed, bridging the gap between automation and human intervention.
