Discover how’s automated machine learning platform helps business analysts leverage data science in their daily operations.


The Road Ahead for Banking

For firms in the financial services or banking industry, staying competitive and compliant while managing high risks is a complex multi-faceted problem. Fighting fraud, analyzing credit risk, improving customer engagement all require a flexible approach to data strategy and business analytics, not old processes. is an automated machine learning platform built to quickly deploy and scale predictive models for banks and financial services.


The Value of Automated Machine Learning

Machine learning techniques can be used to analyze large volumes of transactions and financial data. Leveraging AI-fueled predictive models in trading, corporate finance or consumer banking allows you to plan better, win customer share, and maintain a competitive edge.


Why for Banking

We believe in building highly efficient predictive models that are immediately deployable, scalable, and accessible by users without a data science background. helps financial services firms accelerate their data science projects by empowering business analysts with machine learning models while reducing time spent in implementation.

Start building and deploying machine learning models with

Fraud Detection

Fraud Detection

Fraudsters’ techniques have reached new levels of effectiveness and anonymity. However, leveraging automated machine learning for fraud detection can yield robust results. When suspected, fraud detection algorithms can be used to score a transaction, flag it or reject it instantly. generates predictive models that can identify risk in real-time and prevent fraudulent activities before they happen. View the use case

Loan Recovery

Loan Recovery

Banks are confronted continuously to customers not paying back loans in time. Until now, identifying at-risk clients has relied mostly on debt collectors’ expertise. By using unsupervised machine learning, firms are now able to streamline debt collection, making it more manageable for loan holders, while increasing recovery rates. generates predictive models to optimize loan recovery and drive business for financial services firms View the use case

Credit Scoring

Credit Scoring

Predictive modeling combined with machine learning provides excellent solutions for banks wanting to up their credit scoring capabilities. takes into account all customer data to generate very accurate credit risk models. It empowers lenders to make better loan origination decisions without increasing risk and they understand how credit decisions are made on the platform itself. Automated machine learning accelerates the whole credit scoring process and helps lenders make smarter decisions. View the use case

In 2018, companies will focus on operationalizing AI, particularly in the cloud, to more easily build, refine, deploy and enhance machine learning environments.

– Scott Zoldi, Chief Analytics Officer chez FICO (NYSE: FICO) –
FICO Newsroom, Jan. 11 2018

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