Fintech organizations, whether launched two years ago or 20 years ago, are all vying for the same thing: RESULTS. In all corners of Fintech — be it payments, investing, lending, digital wealth, personal finance, capital markets or one of the myriad other areas — firms are looking to leverage AI and predictive modeling to increase revenues, grow their customer base, improve efficiency and manage risk. The time is now for Fintech firms to take their business to the next level.
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Use Cases Across Fintech
DataRobot is popular with fintechs because it can increase modeling efficiency and accuracy while speeding up fintech operations to give them a competitive advantage over established banks and traditional financial services organizations.
Fintech has fundamentally altered the lending landscape, and machine learning in banking has shined as a game-changing technology for lenders. From making smart underwriting decisions and reducing friction between lenders and consumers to identifying new customers and reducing the churn of existing customer bases, DataRobot’s Enterprise AI Cloud platform helps Fintech lending organizations make better predictions faster.
Improvements in the flow of capital across borders is one of the most significant benefits of Fintech, allowing businesses and consumers to participate in the financial ecosystem in exciting new ways – but significant challenges remain. Fraud has always been a concern in the banking and payments industries. DataRobot’s AI Cloud platform allows companies to build predictive models to identify payment transactions that need closer human inspection. By deploying machine learning models in real-time production, DataRobot helps companies find bad payments before they cause permanent damage.
In an industry dominated by personal wealth advisors, Fintech has begun to automate the interactions between advisors and consumers in a way that increases transparency and reduces transactional fees. Artificial intelligence in Fintech will play a major role in the development of the digital wealth market, addressing the need for increased automation of portfolio management as “robo-advisors” begin to interact more frequently with customers. DataRobot’s AI Cloud platform plays a critical role in aligning consumers with the right opportunities to match their risk tolerance and financial profile.
DataRobot Helps Fintechs With:
Credit Card Fraudulent Transactions
The cost of credit card fraud is billions of dollars per year. By accurately predicting which transactions are likely fraudulent, banks can significantly reduce illegal transactions while providing cardholders with excellent customer experience.
Credit Default Rates
Individuals or businesses often need loans. Making accurate judgments using machine learning and credit risk assessments to mitigate the likelihood of default can make the difference between a successful and unsuccessful loan portfolio.
Digital Wealth Management
Machine learning algorithms help digital wealth advisory companies automate many portfolio management services to be more efficient and effective.
To maximize ROI, it’s important to boost marketing response rates and minimize misdirected communication. The most up-to-date modeling algorithms return the best results, but the data science expertise required to implement them is difficult to come by.