AI and the bottom line: examples of artificial intelligence in finance
If there’s one technology that’s paying dividends, it’s AI in finance. 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.
Source: builtin.com
DataRobot
provides machine learning software for data scientists, business analysts, software engineers, executives and IT professionals.
DataRobot helps financial institutions and businesses quickly build accurate predictive models that enhance decision making around issues like fraudulent credit card transactions, digital wealth management, direct marketing, blockchain, lending and more.
Industry impact:
Alternative lending firm Crest Financial is using DataRobot’s software to make more accurate underwriting decisions by predicting which customers have a higher likelihood of default.
Scienaptic Systems
provides an underwriting platform that gives banks and credit institutions more transparency while cutting losses.
Currently scoring over 100 million customers, Scienaptic’s Ether connects myriad unstructured and structured data, smartly transforms the data, learns from each interaction and offers contextual underwriting intelligence.
Industry impact:
Working with one major credit card company, Scienaptic boasted $151 million in loss savings in just three weeks
Underwrite.ai
analyzes thousands of data points from credit bureau sources to assess credit risk for consumer and small business loan applicants.
The platform acquires portfolio data and applies machine learning to find patterns and determine good and bad applications. Because of its accuracy, Underwriter.ai claims it can reduce defaults by 25–50%.
Industry impact : Since working with Underwriter.ai in 2015, a major online lender providing dental financing reduced its default rate from 17.8% to 5.4%, according to a case study cited on the company’s website.
Managing Risk
Time is money in the finance world, but risk can be deadly if not given the proper attention. Accurate forecasts predictions are crucial to both the speed and protection of of many businesses.
Financial markets are turning more and more to machine learning, a subset of artificial intelligence, to create more exacting, nimble models.
These predictions help financial experts utilize existing data to pinpoint trends, identify risks, conserve manpower and ensure better information for future planning.
The following companies are just a few examples of how AI is helping financial and banking institutions improve predictions and manage risk.
Kensho
provides machine intelligence and data analytics to leading financial institutions like J.P. Morgan, Bank of America, Morgan Stanley and S&P Global.
Kensho’s software offers analytical solutions using a combination of cloud computing and natural language processing (NLP). The company’s systems can provide answers to complex financial questions in plain English.
Industry impact:
Traders with access to Kensho’s AI-powered database in the days following Brexit used the information to quickly predict an extended drop in the British pound, according to a 2017 Forbes article.
In March 2018, S&P Global announced a deal to acquire Kensho for roughly $550 million.
Ayasdi
creates cloud-based and on-premise machine intelligence solutions for enterprises and organizations to solve complex challenges.
For companies in the fintech space, Ayasdi is deployed to understand and manage risk, anticipate the needs of customers and even aid in anti-money laundering processes.
Industry impact:
Ayasdi is helping banks combat money laundering with its anti-money laundering (AML) detection solutions. The sheer volume of investigations has been a major strain on financial institutions. Using the company’s AML solution, one major bank saw a 20% reduction in investigative volume , according to Ayasdi.
1. Automation for AI in Banking
With AI technology, it’s possible to automate processes to manage tasks like understanding new rules and regulations or creating personalized financial reports for individuals. For example, IBM’s Watson can understand complex regulations , such as additional reporting requirements of the Markets in Financial Instruments Directive and the Home Mortgage Disclosure Act. Rather than asking financial professionals to research answers to questions, which can take hours and days, Watson can find the answer in mere moments Similarly, wealth managers can use AI to generate more in-depth status reports for their clients quicker, which allows them to provide individualized advice to more clients. Not only that, they can do it faster and present the information in a way that’s easier to understand. Finally, AI allows bankers to make loan decisions in seconds, not months, assessing risks and spending patterns, and even looking at alternative sources of data, such as payment history of rent and utilities. By automating the decision making process, bankers can reduce their risk of default loans, as well as improve customer experience by reducing the number of abandoned applications from frustrated borrowers who are tired of the long process.
Source: yodlee.com
2. AI-Based Reporting and Analysis
Just ten short years ago, if you wanted to check your bank balance, you had to log onto your computer, visit your bank’s website, and look for yourself. If you wanted to know the state of your household budget, you had to look at the spreadsheet you created for yourself. Now with mobile banking apps and web portals, financial service AI specifically Envestnet | Yodlee’s® AI Fincheck can analyze consumers’ individual 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, keeping up with real-time changes and then creating customized advice based on new incoming data. Envestnet Intelligence, advanced analytics for financial institutions , enables 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.
Source: yodlee.com
3. Transaction Data Enrichment
This is an important part of financial management, both for financial institutions and consumers. Our Transaction Data Enrichment (TDE) uses machine learning and artificial intelligence to decipher unintelligible strings of characters that represent transactions and merchants and converts them to readable text that shows each merchant’s name and lists their address and city. It shows the local merchant’s location, rather than the central corporate office. This method of turning hard-to-understand data into easy-to-read information, helps both banks and customers to understand where they spent their money and with whom. It reduces both customer service calls and fraud research costs, because the customers can tell what they bought and where they bought it. Fraud detection reduces the number of people calling about mystery charges on their credit card bill, because they understand what those charges mean. Fewer calls means less fraud research, which reduces costs. Most importantly, these clear descriptions help developers put financial data into context so they can more easily categorize and analyze purchases. This helps with things like budgeting, analyzing spending habits, credit scoring and being able to predict future earning and spending issues.
Source: yodlee.com
4. Predictive Analytics
When it comes to financial advice, many consumers want some help when it comes to personal finance advice. A recent study by Aite Group showed that 79% of 22–34 year olds, 77% of 35–49 year olds, and 62% of 50+ year olds were moderately-to-extremely interested in using a digital financial wellness coach. But they don’t just want abstract lessons about finance. Consumers want to be warned and reminded of important information about their own financial data, not told about issues after the fact. They want to be advised when they should and shouldn’t make purchases, not be sent an alert when they’ve accidentally overdrawn their accounts. For example, our OK to Spend financial forecasting tool financial forecasting tool tells users when they can actually spend money, based on their income, bank balances, upcoming obligations. The tool uses AI and machine learning, predictive analytics, and even user feedback to predict future outcomes. It helps users make smart decisions based on their financial picture at the moment This way, consumers who ask about a purchase “Can I buy this today?” can get a yes or no answer that will help them avoid problems like overdrafts, late fees, and end-of-the-month shortfalls. How to create a personalized and predictive digital banking experience.
Source: yodlee.com
5. 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. But as it grows, it will begin to learn the different language and terms people use to describe the same process. Similarly, as more and more financial institutions develop voice applications, the chatbots will need to recognize vocal pitches, inflections, pronunciations, and accents. Learn how chatbots fuel banking customer engagement.
Learn More About Artificial Intelligence Use Cases
These artificial intelligence use cases have revolutionized the financial industry and changed the way we access, analyze, and understand information. Banks and third-party developers who want to take advantage of these advancements can offer exciting new products and features to their users.
Source: yodlee.com