Discover how enterprise AI is driving the Fintech revolution.
DataRobot captures the knowledge, experience, and best practices of the world’s leading data scientists, delivering unmatched levels of automation and ease-of-use for enterprise AI initiatives. From AI in banking, to AI in asset management, to AI in credit risk: DataRobot enables Fintech users and companies to build and deploy highly accurate enterprise AI models in a fraction of the time.
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 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 enterprise AI 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 enterprise AI platform plays a critical role in aligning consumers with the right opportunities to match their risk tolerance and financial profile.
Credit Card Fraudulent TransactionsThe 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 RatesIndividuals 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 ManagementMachine learning algorithms help digital wealth advisory companies automate many portfolio management services to be more efficient and effective.
Direct MarketingTo 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.
VP, Data Analytics, Wellen Capital
At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
DataRobot allows us to understand the data that’s being fed into our models without blindly feeding whatever we get into our system. DataRobot makes my team very effective.