Reshaping Fintech with AI: Top AI Innovations in the Financial Industry

Check out the top 10 AI innovations in the financial industry and their future predictions

Artificial intelligence has given the world of banking and the financial industry as a whole a way to meet the demands of customers who want smarter, more convenient, safer ways to access, spend, save and invest their money. Financial and banking procedures have been made easier because of the use of AI and machine learning. FinTech companies are particularly interested in AI, either to develop it or to utilize it themselves, because it has so many useful applications. AI development solutions are aimed at meeting the critical needs of today’s financial sector, such as improved client experience, cost-effectiveness, real-time data connectivity, and increased security. The industry can develop a better, more engaging financial environment for its clients by implementing AI and related technologies. This article lists the top 10 AI innovations in the financial industry. 

Credit Scoring Model: This AI model is developed by the company Temenos AI. Temenos is the first to bring transparency and explainability of AI automated decision-making to the banking industry. The credit scoring model reduces credit risk with the ability to increase pass rates while maintaining or reducing current default risk. Integrated with Temenos Infinity, it takes manual underwriting to the next level through AI automated decision-making and recommendations that are transparent and explainable to the end-user.

Smart Data Lake: The XAI platform also developed by Temenos AI is fully integrated with the Temenos Data Lake, giving banks a real-time, end-to-end Smart Data Lake, offering higher quality and richness of data through multiple sources. The Data Lake approach refers to assembling large amounts of diverse data from a multitude of data sources, retaining their original model and format, and allowing users to query and analyze them in situ. This means that banks can make faster, more accurate, and explainable decisions driven by AI algorithms.

Robo-Advisors: Robo-advisors are not only low-cost alternatives to traditional financial advisors but they can also facilitate financial counseling for a large group of people, helping to make more informed financial decisions. Besides, data-driven AI-powered Robo-advisors can also recommend investors on scaling their portfolio, retirement, estate planning, etc., which in turn can make the account opening process an interactive experience.

AI-Based Reporting and Analysis: Now with mobile banking apps and web portals, financial service AI specifically Envestnet Yodlee’s AI Fincheck can analyze consumers’ account data to see what they have, how they’re performing financially, make recommendations on future actions based on the results, and then help with automation for savings and budgeting for better financial health and behavior. In the finance industry, AI can be used to examine cash accounts, credit accounts, and investment accounts to look at a person’s overall financial health, keep up with real-time changes, and then create customized advice based on new incoming data.

Envestnet Intelligence and Advanced Analytics: Envestnet intelligence and advanced analytics for financial institutions, enable financial institutions to easily get answers in real-time to key business questions across desktop, mobile, and Amazon Alexa-enabled devices. Providing interactive, predictive, and conversational capabilities, Envestnet Intelligence extracts information from comprehensive financial data sets to ensure financial institutions have an easy way to answer crucial questions anywhere, anytime, on any device.

Chatbots: Chatbots in banking are not only a money-saving tool, they can automate simple tasks such as opening a new account or transferring money between accounts. Companies that want to use them only need to install them on their existing websites rather than create a separate chatbot app. And they’re always on, so even a customer who visits your website at 3:00 AM can get answers to their questions and assistance with their problems. Programming a chatbot means starting with specific tasks it can perform, such as paying a bill or processing an account application.

Quick and Scalable Graph Platforms: The TigerGraph graph platform is the next level in AI software and machine learning tools for graph databases. TigerGraph combines features such as Massively Parallel Processing, MapReduce, and fast data compression and decompression with new approaches. Combining these features creates a scalable, quick, and reliable means of deep exploration. This allows the user to get the maximum value from their data. TigerGraph’s tools utilize analytics, machine learning, and AI algorithms to help analyze complex data sets. Leading financial service providers such as Visa and corporate and personal service provider China Merchant Bank use TigerGraph to enhance their fraud detection processes.

Potential Future Simulator in Virtual Environment: Enterprise software producers Simudyne produce AI software for financial institutions. Their solutions allow financial service providers to efficiently simulate potential future scenarios in a secure, virtual environment. Their AI software can be applied across trading, lending, and risk management areas. This AI tool allows banks to simulate a range of scenarios, such as modeling the actions of a fraudster.

Software Robotics: ICICI Bank, India’s second-largest private sector bank has deployed software robotics in over 200 business processes across various functions of the company. The software robots at ICICI Bank are configured to capture and interpret information from systems, recognize patterns, and run business processes across multiple applications to execute activities, including data entry and validation, automated formatting, multi-format message creation, text mining, workflow acceleration, reconciliations and currency exchange rate processing among others.

Predictive Analytics and Predictive Banking: The predictive banking features include alerting customers of higher-than-average recurring billing payments, reminding a customer to transfer money into their savings account if they have more money than average in their checking account, and prompting customers to set up a travel plan for their account after they’ve purchased a plane ticket. Predictive banking can provide mobile app users with over 50 different prompts for various scenarios. For example, if a customer receives an incoming deposit that is not in their usual pattern of transactions and is not needed to meet their normal expenses or scheduled payments, the system can highlight the deposit and suggest the customer save the funds.

The Future of AI in the Financial Industry

Whether in accelerated trading, automated call centers, real-time fraud prevention, or other financial services, AI is helping financial institutions drive the future of finance for their customers and clients. Ultimately, financial institutions will AI-enable hundreds, if not thousands, of applications. Those banks that invest in enterprise AI transformation stand to gain market share, improve customer satisfaction and improve their financial performance at the expense of those that fail to innovate in AI.

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