The financial sector is leading the adoption of AI, which has the potential to add huge value to the industry. 48% of banks with over $50 billion of assets have deployed an AI solution, and a third of all financial services providers have implemented AI in some form. The biggest buzz surrounds machine learning and deep learning for the financial sector, with an enormous improvement expected for use cases such as fraud detection, risk mitigation, and investment optimization. 

But despite the excitement, financial experts are still struggling to deliver on the AI vision, due to a combination of the scarcity of data science resources and stringent regulations surrounding AI usage. 

With’s automated machine learning platform, financial organizations can stay ahead of the market and meet regulatory requirements. By working with, they can easily build fully transparent predictive models without needing a robust data science operation. Here are three of the most powerful use cases for AutoML in the finance industry.

Risk Mitigation

AI-powered credit scoring models enable lending institutions to increase both speed and accuracy in risk mitigation, thereby reducing the rate of non-performing loans and raising loan acceptance rates. automates the process of building, testing, and deploying credit scoring models, based on the applicant’s credit history. The platform makes it quick and easy to leverage machine learning, even without a large data science department.

AI-powered risk mitigation is based on rules that are more complex and subtle than those used by traditional scoring models. Machine learning models also strip out human bias. Being able to distinguish between high-risk applicants and those who are creditworthy, even when the applicants don’t have a long credit history, helps lenders to extend more loans without compromising their risk profile.

When building risk mitigation models, lenders have to meet regulatory requirements such as model transparency. They have to be able to explain the reasons behind the decision to extend or deny a loan or a credit card. The trouble is that many machine learning models are not easy to interpret and explain. Fortunately, models built on are fully transparent, for total compliance and easy explainability. 

Investment Optimization

Successful investments are always time-sensitive. The faster you can react to significant trading news and carry out your investment transaction, the more money you’re likely to make. Machine learning models can process structured data from financial markets in a fraction of the time that it takes a human, and generate data-based investment predictions in seconds. allows you to use machine learning to understand and respond to integrated data from markets, firmographics, and alternative and event-driven data, to significantly improve your portfolio’s performance. 

Investment experts can use to build reliable, time-based predictive models for investments. Take long-term and short-term goals into consideration, along with seasonality, periodicity, and any other relevant markers to build a time series model that enables optimized investments.

Fraud Detection

Fraud detection is critical in the finance industry, but traditional methods aren’t powerful enough to cope with today’s fraudsters. Currently, most financial organizations rely on elementary, rule-based engines to prevent internal and external fraud. These rules can be effective, but their accuracy drops when scaling. Adding a machine learning layer on top of the rule-based engine can strengthen fraud prevention while reducing the incidence of false alerts. 

Rule-based engines are absolute, only recognizing yes or no answers. But spotting fraudulent behavior demands the ability to grade activities along with a spectrum, from the least to the most suspicious.

A rule-based engine is, by definition, static. It can’t adapt to changing circumstances. In contrast, a machine-learning model constantly responds to changes based on new data, remaining relevant when behavior patterns alter. This is crucial because fraudsters’ tactics are always evolving and quickly bypass rule-based engines. Only machine learning enables the system to adapt quickly to detect fraud on an ongoing basis. Working with’s automated machine learning platform gives financial organizations the capabilities to instantly respond to new threats, delivering new predictive models within hours. brings new possibilities for the finance industry

AutoML brings transformative possibilities for the finance industry. By incorporating machine learning, financial institutions can save time and money on improved data-driven decision-making, faster credit decisions, and a radical drop in losses due to fraud.’s simple, automated platform allows financial institutions to access the power of machine learning quickly and easily, without the need for advanced data science experience.