Machine Learning for Financial Services

Financial Services companies need to continuously advance fraud detection capabilities and provide transparency behind every credit and loan decision made to comply with current or pending legislation. simMachines provides game changing machine learning software that drives significant marketing performance lift, fraud detection and compliance with full transparency behind every prediction.

“We have a whole department that gives a propensity to take an action on each of our accounts. If there’s something that can give us a better answer, it would be incredibly valuable.”

– Sr. Manager of Marketing Strategy, Financial company

Capture The Customer Moment With Dynamic Predictive Segmentation, a January 2018 commissioned study conducted by Forrester Consulting on behalf of simMachines

Revealing the Why Behind Every Prediction

simMachines provides the following applications for global financial institutions, banks and insurance companies. We provide the Why factors behind every prediction and can expose the Why behind any other machine learning method. simMachines is the only machine learning technology that can provide transparency behind every prediction at the local vs. global level.

Dynamic Predictive Card/Load Holder Segmentation

Reveal granular machine driven customer clusters instantly for greater marketing and advertising campaign precision and speed.

Customer Opinion Clustering

Find and group distinctive social media & call center comments to uncover the patterns in what customers actually think.

Customer Lifecycle Predictions

Achieve 20% – 100% lift in analytic performance across all dimensions of the shopper lifecycle.

Fraud Prevention

Reduce eCommerce fraud by 25% – 100% while revealing the factors to identify fraud transactions.

Product Demand Predictions

Forecast demand at a product / category level to enable highly relevant product campaigns and recommendations

Customer Experience Predictions

Leverage predictions and their driving factors to anticipate the context and next best action of every customer interaction.

Risk Analysis

Assess risk for new customers or transactions based on similar previously identified high risk situations.

Legislative Compliance

Create an auditable record of algorithm driven credit or insurance decisions and the underlying factors to comply with current or pending legal requirements.

See it in action

Use Cases

simMachines supports financial services clients across marketing, fraud and compliance use cases. Fraud detection machine learning is a primary area that financial service companies benefit from knowing why fraud is occurring, to adjust business strategies for future prevention.

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.

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.

Compliance Monitoring

Global Exchange company deployed method of monitoring OTC submissions for rules-infringing transactions lacked sensitivity and required significant human capital.

Recent news in financeALL NEWS ON OUR BLOG

Danny Shayman | 26, September

Dynamic Customer Segmentation: The Next Evolution in Marketing The current state of customer segmentation is quite antiquated in an age where customers expect immediate relevant communication from brands regularly.

How Explainable Machine Learning Enhances Credit Card Fraud Prevention
Dave Irwin | 24, April

Advertising is more data driven today than ever before. Digital advertising has evolved over the last ten years to define audiences at much greater levels of granularity.

Uncover Actionable Insights Using XAI
Dave Irwin | 28, February

Advertising is more data driven today than ever before. Digital advertising has evolved over the last ten years to define audiences at much greater levels of granularity.

Learn more about working with simMachines