Key AI Innovations in the Financial Sector


Discover the top 10 AI innovations in the financial sector 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 and secure ways to access, spend, save and invest their money. Financial and banking procedures have been simplified through the use of AI and machine learning. FinTech companies are particularly interested in AI, either to develop it or to use it themselves, because it has many useful applications. AI development solutions aim to address the critical needs of today’s financial industry, such as improved customer experience, cost efficiency, real-time data connectivity, and increased security. The industry can develop a better and more engaging financial environment for its customers by implementing AI and related technologies. This article lists the top 10 AI innovations in the financial industry.

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

Smart Data Lake: The XAI platform also developed by Temenos AI is fully integrated with the Temenos Data Lake, providing banks with an end-to-end real-time Smart Data Lake, delivering superior data quality and richness through multiple sources . The Data Lake approach involves assembling large amounts of diverse data from a multitude of data sources, retaining its original model and format, and allowing users to query and analyze it in situ. . This means banks can make faster, more accurate and more explainable decisions thanks to AI algorithms.

Robo-advisors: Robo-advisors are not only low-cost alternatives to traditional financial advisors, they can also facilitate financial advice for a large group of people, helping to make more informed financial decisions. In addition, AI-powered and data-driven robo-advisors can also recommend investors to evolve their portfolio, retirement, estate planning, etc., which in turn can make the opening process an interactive experience.

AI-powered reporting and analytics: Now, with mobile banking apps and web portals, financial services AI, specifically Envestnet Yodlee’s AI Fincheck, can analyze consumer account data to see what they have, their financial performance, make recommendations on future actions based on the results, and then help automate savings and budgeting for better financial health and behavior. In the financial industry, AI can be used to review cash accounts, credit accounts, and investment accounts to examine a person’s overall financial health, track changes in real time, and then create personalized advice based on new incoming data.

Envestnet Intelligence and Advanced Analytics: Envestnet Intelligence and Advanced Analytics for Financial Institutions makes it easy for financial institutions to get real-time answers to key business questions across desktop, mobile, and Amazon Alexa-enabled devices. Offering interactive, predictive and conversational capabilities, Envestnet Intelligence extracts insights from comprehensive financial datasets to ensure financial institutions have an easy way to answer critical questions anywhere, anytime, on any device. .

Chatbots: Chatbots in banking are not just a money-saving tool, they can automate simple tasks such as opening a new account or transferring money between accounts. Businesses that want to use them just need to install them on their existing websites rather than creating a separate chatbot app. And they’re always on, so even a customer who visits your website at 3 a.m. can get their questions answered and help with their issues. Programming a chatbot means starting with specific tasks it can perform, such as paying a bill or handling an account request.

Fast 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 like massively parallel processing, MapReduce, and fast data compression and decompression with new approaches. The combination of these features creates a scalable, fast, and reliable means of deep exploration. This allows the user to get the maximum value from their data. TigerGraph’s tools use analytics, machine learning, and AI algorithms to help analyze complex data sets. Leading financial services providers such as Visa and business and consumer services provider China Merchant Bank use TigerGraph to improve their fraud detection processes.

Potential future simulator in a virtual environment: Simudyne enterprise software producers produce AI software for financial institutions. Their solutions enable financial service providers to effectively simulate potential future scenarios in a secure virtual environment. Their AI software can be applied in the areas of trading, lending, and risk management. This artificial intelligence tool allows banks to simulate a range of scenarios, such as modeling the actions of a fraudster.

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

Predictive analytics and predictive banking: Predictive banking features include alerting customers to above-average recurring bill payments, reminding a customer to transfer money to their savings account if they have more more money than the average in its current account and the incentive for customers to define a travel plan for their account after having 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 does not match their usual pattern of transactions and is not needed to meet their normal expenses or scheduled payments, the system may highlight the deposit and suggest the customer to save the funds.

The future of AI in the financial sector

Whether it’s 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 customers. Ultimately, financial institutions will enable AI for hundreds, if not thousands, of applications. Banks that invest in enterprise AI transformation are likely to gain market share, improve customer satisfaction, and improve financial performance at the expense of those that do not innovate in AI .


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