ModelOps to Modernize Bank Loan Departments

Introduction ModelOps in Lending

Banks provide loans to customers in exchange for future repayment of the lended amount with additional interest. A bank's lending department goes through a long process to determine whether a loan candidate is worth providing a loan to or not. To ensure profitability, it is important that the lending department only provides loans to customers who are less likely to default on their repayments. The information review often involves the candidate's credit history, financial assets, loan amount and duration, interest rate, housing situation, etc.


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Machine Learning to aid Lending Decisions

Previously the decision-making for choosing eligible candidates was done manually with loan agents going over the records and candidate interviews, judging from experience and records to make their decisions. But, with the rise in machine learning and automation, banking loan departments have adapted technical means to analyze the candidate details and make decisions by comparing them to past records. 

Using machine learning for analyzing loan candidate details is an effective measure because:

  • ML models perform analysis much faster and are free from human error, negligence and biases, thus more accurate in their predictions.
  • Machine learning models can utilize a vast range of statistical and mathematical tools to learn from customer details and their default status records and show relationships between different variables. This relationship can be linear or non-linear.
  • Utilizing the ML approach also solves the problem of dimensionality and scalability since machine learning can use large datasets to make predictions which other approaches cannot.
  • Inference gained from ML models can be shared widely and are backed by mathematical reasoning.

Classification models have proven to be very effective in classifying potential loan candidates as likely to default or not. Statistical models like Logistic Regression, Decision Trees, Random Forests, xgboost have been trained and tested on datasets of past loan candidates with 100s of features. These models are then used to classify real-time candidates based on their records.

Once the models are trained and tested, they can be deployed in the bank's loan department to aid decision-making. To ensure that the developed model works effectively and adds value to the decision-making process, it is important to regularly monitor and update the model. And architectures like Model Operations are designed to handle such operations.


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ModelOps for Lending Models

Often, bank loan departments have a highly accurate model, but they cannot operationalize it. Often model operations include a collaboration between ML operations, IT operations, and Cloud and Dev operations. From building and deploying the model to regular monitoring and auditing, there is a need for well-established Model operations to handle the model life-cycle.

In case the lending classification model loses its accuracy with new data (also known as model drift), regular monitoring can alert the authorities and developers to take action. In such situations, models are either re-trained or discontinued altogether.

There can be instances when the loan approval model has been deployed on the cloud, and some latency issue occurs in the future. In such cases, the data scientist would not be authorized to access the cloud infrastructure. Thus, ModelOps solves this problem.

ModelOps can help scale the models as well. Since these operations are not isolated to one banking sector or department, data and insights from other departments can be integrated into the model, and the model inferences can be shared with other departments. Integration of Cloud operations aid in sector-wide model governance and monitoring of the model.


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Setting up ModelOps Infrastructure

To scale their artificial intelligence efforts and apply their developed models to 100s of use cases, bank loan departments must set up a ModelOps Infrastructure. This often involves building the architecture from scratch and integrating different services that different departments use. It can be confusing and time-consuming without proper experience and guidance in the ModelOps architecture.

Another way of integrating ModelOps infrastructure in the model production efforts is to consult third-party providers to partner with the banking department and build the infrastructure according to their needs.

Consulting ModelOps platforms can be an effective way to integrate this technology. ModelOps platforms are online platforms that provide end-to-end model governance services on their servers. Users can build their models and personalize their production pipeline by choosing from the various tools available.

Conclusion

Bank loan departments use machine learning models for decision-making in choosing eligible loan candidates since machine learning provides many benefits over manual analysis problems. A collaboration between different departments is needed to operationalize the machine learning models through different branches and departments of the bank. ModelOps solves the disconnection between different operational departments. ModelOps infrastructure provides an eco-system to govern a model's life cycle to ensure that the model is monitored and audited regularly and provides desired value to the customers.