RESHAPING FINTECH WITH AI: TOP AI INNOVATIONS IN THE FINANCIAL INDUSTRY
13 December 2021
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Artificial intelligence has given the world of 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 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. This article lists the top 10 AI innovations in the financial industry.
1) Credit Scoring Model: The credit scoring model reduces credit risk with the ability to increase pass rates while maintaining or reducing current default risk. It takes manual underwriting to the next level through AI automated decision-making and recommendations that are transparent and explainable to the end-user.
2) Data Lake: The Data Lake is a large storage repository that holds a huge amount of “raw data” in its original format until data scientists need it. This approach is different from “Data Warehouse” that deal with “processed data” and for decades, foundation for business intelligence and data discovery/storage rested on data warehouses. Data Lake provide great extent of scalability at reasonable cost, fast, and integration with emerging trends such as Internet of Things (IoT). Data Lake can play major role in driving innovative FinTech models that rely on predictive AI and collection of huge amount of insights yet, data integrity and storage location might impose regulatory constraints and challenges. Thus, Data Lake can be positioned for the research purpose but not the ultimate data storage.
3) 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.
4) AI-Based Reporting and Analysis: Now with mobile banking apps and web portals, financial service AI 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.
5) Advanced Analytics: The use of advanced analytics for financial institutions through data aggregation and data analytics platforms, enable financial institutions to easily get answers in real-time to key business questions across desktop, mobile devices. Providing interactive, predictive, and conversational capabilities, advanced analytics platforms extracts information from comprehensive financial data sets to ensure financial institutions have an easy way to answer crucial questions anywhere, anytime, on any device. It also, identify cross-sell and upsell opportunities; decide how to competitively position products and services; choose which marketing campaigns to run; determine how to segment customers; and know how to engage those customers.
6) Chatbots: Chatbots in banking are not only a money-saving tool, they can automate simple tasks. 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.
7) Quick and Scalable Graph Platforms: The graph platforms are the next level in AI software and machine learning tools for graph databases. Where it 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. Graph Platforms utilize analytics, machine learning, and AI algorithms to help analyze complex data sets. Leading financial service providers to enhance their fraud detection processes.
8) Potential Future Simulator in Virtual Environment: This solution allow financial service providers to efficiently simulate potential future scenarios in a secure, virtual environment. This 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.
9) Software Robotics: The software robots 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.
10) 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 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.
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