Artificial intelligence is currently the cool kid on the technology block and everyone is vying for a seat at the popular table, where AI reigns supreme with its posse of algorithms and insights.
One group with a comfortable seat at that table is finance and banking, industries that are especially reaping the benefits of AI technology.
As customers demand smarter, more convenient and safer ways to access, spend, save and invest their money, the financial world is looking to artificial intelligence to give them what they want.
We've put together a rundown of how AI is being used in finance and the companies leading the way.
Credit is king. A recent study found 77% of consumers preferred paying with a debit or credit card compared to only 12% who favored cash. But easier payment options isn't the only reason the availability of credit is important to consumers.
Having good credit aids in receiving favorable financing options, landing jobs and renting an apartment, to name a few examples. With so many of life's important necessities hinging on credit history, the approval process for loans and cards is more important than ever.
Artificial intelligence solutions are helping banks and credit lenders make smarter underwriting decisions by utilizing a variety of factors that more accurately assess traditionally underserved borrowers, like millennials, in the credit decision making process.
Here are a few examples of companies helping the financial industry rethink the underwriting process.
Location: Los Angeles
What they do: ZestFinance is the maker of the Zest Automated Machine Learning (ZAML) platform, an AI-powered underwriting solution that helps companies assess borrowers with little to no credit information or history.
Finance application: The platform utilizes thousands of data points and provides transparency that other underwriting systems cannot, which helps lenders better assess populations traditionally considered "at risk." ZAML is an end-to-end platform that institutions can implement and scale quickly.
Real-life use case: Auto lenders using machine-learning underwriting cut losses by 23% annually, more accurately predicted risk and reduced losses by more than 25%, according to ZestFinance.
What they do: Underwrite.ai analyzes thousands of data points from credit bureau sources to assess credit risk for consumer and small business loan applicants.
Finance application: 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%.
Real-life use case: 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.
What they do: In addition to other financial-based services, Scienaptic Systems provides an underwriting platform that gives banks and credit institutions more transparency while cutting losses.
Finance application: 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.
Real-life use case: Working with one major credit card company, Scienaptic boasted $151 million in loss savings in just three weeks.
What they do: DataRobot provides machine learning software for data scientists, business analysts, software engineers, executives and IT professionals.
Finance application: 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.
Real-life use case: 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.
Quantitative trading is the process of using large data sets to identify patterns that can be used to make strategic trades. Artificial intelligence is especially useful in this type of trading.
AI-powered computers can analyze large, complex data sets faster and more efficiently than humans. The resulting algorithmic trading processes automate trades and save valuable time.
The following companies are just a few examples of how AI-infused technology is helping financial institutions make better trades.
Location: Bellevue, Wash.
What they do: Kavout uses machine learning and quantitative analysis to process huge sets of unstructured data and identify real-time patterns in financial markets.
Finance application: One of Kavout's solutions is the Kai Score, an AI-powered stock ranker. The Kai Score analyzes massive amounts of data, such as SEC filings and price patterns, then condenses the information into a numerical rank for stocks. The higher the Kai Score, the more likely the stock will outperform the market.
Real-life use case: According to a recent analysis, Kai's "top picks portfolio" boasts a 21.9% compound annual growth rate (CAGR) since 2012, vastly outperforming the S&P 500's 13.3% CAGR.
Location: San Mateo, Calif. (U.S. office)
What they do: Alpaca combines proprietary deep learning technology and high-speed data storage to provide short and long-term forecasting applications.
Finance application: Alpaca’s technology identifies patterns in market price-changes and translates its findings into multi-market dashboards.
Real-life use case: The company recently partnered with financial news giant Bloomberg to provide users with its "AlpacaForecast AI Prediction Market." The program predicts short-term forecasts in real-time for major markets.
Traditional banking isn't cutting it with today's digital savvy consumers.
A study by Accenture of some 33,000 banking customers found 54% want tools to help them monitor their budget and make real-time spending adjustments. Additionally, 41% are "very willing" to use computer-generated banking advice.
AI assistants, such as chatbots, use artificial intelligence to generate personalized financial advice and natural language processing to provide instant, self-help customer service.
Here are a few examples of companies using AI to learn from customers and create a better banking experience.
What they do: Kasisto is the creator of KAI, a conversational AI platform used to improve customer experiences in the finance industry.
Finance application: KAI helps banks reduce call center volume by providing customers with self-service options and solutions. Additionally, the AI-powered chatbots also give users calculated recommendations and help with other daily financial decisions.
Real-life use case: TD Bank Group announced plans to integrate Kasisto's technology into their mobile app, providing customers with real-time support and spending insights.
Location: Orlando, Fla.
What they do: Abe AI is a virtual financial assistant that integrates with Google Home, SMS, Facebook, Amazon Alexa, web and mobile to provide customers with more convenient banking.
Finance application: The assistant provides services ranging from simple knowledge and support requests to personal financial management and conversational banking.
Real-life use case: In 2016 Abe released its smart financial chatbot for Slack. The app helps users with budgeting, savings goals and expense tracking.
Location: San Francisco
What they do: Trim is a money-saving assistant that connects to user accounts and analyzes spending.
Finance application: The smart app can cancel money-wasting subscriptions, find better options for services like insurance, and even negotiate bills.
Real-life use case: Trim has saved $6.3 million for more than 50,000 people, according to a 2016 VentureBeat article.
Cybersecurity & Fraud Detection
Every day, huge quantities of digital transactions take place as users move money, pay bills, deposit checks, trade stocks and more via online accounts and smart phone applications.
The need to ramp up cybersecurity and fraud detection efforts is now a necessity for any bank or financial institution, and artificial intelligence is playing a key role in improving the security of online finance.
Here are a few examples of companies providing AI-based cybersecurity solutions for major financial institutions.
Location: Mountain View, Calif.
What they do: Utilized by top banks in the U.S., Shape Security curbs credit application fraud, credential stuffing, scraping and gift card cracking by pinpointing fake users.
Finance application: The company's machine learning models are trained on billions of requests, allowing the software to effectively distinguish between real consumers and bots. Shape Security's Blackfish network also uses AI-enabled bots to detect compromised login credentials, alerting both customers and companies to security breaches instantly.
Real-life use case: Shape's solutions have helped one major bank protect customers from account highjacking and detected one million credential stuffing attacks in the first week of use, according to information provided on the company's website.
Location: Cambridge, Mass.
What they do: Darktrace creates cybersecurity solutions for a variety of industries and financial institutions are no exception.
Finance application: The company's machine learning platform analyzes network data and creates probability-based calculations, detecting suspicious activity before it can cause damage for some of the world’s largest financial firms.
Real-life use case: In a highlighted case study on the company's website, global financial software firm Ipreo deployed Darktrace to protect its customers from sophisticated cyber attacks. Ipreo saw immediate results in real-time threat detection and defense against internal and external threats.
Location: San Jose, Calif.
What they do: Vectra is the company behind Cognito, an AI-powered cyber-threat detection and hunting solution.
Finance application: Vectra's platform automates threat detection, reveals hidden attackers specifically targeting financial institutions, accelerates investigations after incidents, and even identifies compromised information.
Real-life use case: A Vectra case study provides an overview of its work to help a prominent securities exchange prevent malware attacks. Cognito immediately identified a misconfiguration in the exchange's authentication systems that would have otherwise gone unnoticed.
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.
Location: Menlo Park, Calif.
What they do: Ayasdi creates cloud-based and on-premise machine intelligence solutions for enterprises and organizations to solve complex challenges.
Finance application: 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.
Real-life use case: 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.
Location: Cambridge, Mass.
What they do: Kensho provides machine intelligence and data analytics to leading financial institutions like J.P. Morgan, Bank of America, Morgan Stanley and S&P Global.
Finance application: 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.
Real-life use case: 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.