Machine Learning Use Cases in Finance
simMachines supports financial services clients across a variety of use cases.
Customer Acquisition Credit Worthiness
An international bank client provides loans to small businesses. Our client needed a custom, predictive engine that would help quickly determine the credit worthiness of a small business owner.
Many systems and methods have been implemented to determine if a person is likely to pay back a loan or if they are capable of owning a credit card. The problem with these methodologies is that they are based on general models and theories that may not closely match the reality of a specific financial institution.
simMachines implemented an algorithm that could achieve the following:
- Predict the type of new customer being contacted: A++, A+, A, B,C,D
- Predict the number of times an operator would have to call the customer in order to collect payments
simMachines successfully created and deployed the solution in less than two weeks.
Machine Learning Fraud Detection
Top 3 financial institution wanted to speed up ability to implement ML fraud detection solutions for clients, enable continuous learning, and expose the factors driving the fraud.
ML methods did not continuously learn or expose the Why factors. Increased competition was creating a need to upgrade speed and service value to client.
Our learned metric, similarity-based machine learning solution provides engines custom modeled for each of our partner’s clients.
70% increase over current fraud detection performance. Time to implement reduced from 6 weeks to 2 weeks. Continuous learning enabled, and “the Why” factors reported/available behind every prediction.
Explainable Anomaly Detection
Deployed method of monitoring OTC submissions for rules-infringing transactions lacked sensitivity and required significant human capital
Sparse clusters, inaccurate training data
Algorithm that provides high precision while explaining Why a transaction is flagged as illegitimate
50% increase in illegitimate transactions detected, 30% reduction in false positive rate