https://sigmoidal.io/real-applications-of-ai-in-finance/
Artificial Intelligence is taking the financial services industry by storm. Almost every company in the financial technology sector has already started using AI to save time, reduce costs, and add value.
For example, robo-advisor Wealthfront tracks account activity using AI capabilities to analyze and understand how account holders spend, invest, and make financial decisions, so they can customize the advice they give their customers.
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Funders are taking notice. In January, CB Insights reported that 2016 was a record year for AI venture funding deals With 550 companies using AI in their products raising $5 billion. In July, CB Insights announced a list of 250 companies that are poised to redefine the financial services industry. At the 2016 Annual Technology Innovation Summit, hosted by Bank of America and Merrill-Lynch in Silicon Valley, AI, robotics, and VR were named as the most interesting area in technology for the coming year.
The seven leading U.S. commercial banks have prioritized strategic technological advancement by investing in AI applications to better serve their customers, improve performance, and increase revenue. For example, JPMorgan Chase’s Contract Intelligence (COiN) platform uses image recognition software to analyze legal documents and extract important data points and clauses in seconds, compared to the 360,000 hours it takes to manually review 12,000 annual commercial credit agreements.
Attendees inspect JPMorgan Markets software kiosk for Investors Day.
Photographer: Kholood Eid/Bloomberg
Wells Fargo began piloting an AI-driven chatbot that communicates with users to provide account information and helps customers reset their passwords through Facebook Messenger in April 2017. And Bank of America reported a $3 billion innovation budget in 2016.
The future of finance will be heavily influenced by emerging fintech companies and AI technology applications setting the stage for increasing competitiveness among the industry’s leading giants. In the next decade, Artificial Intelligence will help financial services companies maximize resources, decrease risk, and generate more revenue, in the trading, investing, banking, lending, and fintech verticals.
Maximizing Resources
Artificial Intelligence helps companies in the financial industry save time and money through the use of algorithms to generate insights, improve customer service, and make predictions about company sales performance and churn.
Unlocking the value of AI algorithms
Automation, which has been used in factory processes for decades, is about replacing repetitive tasks with machines: Software has automated tasks, like matching data records, looking for exceptions, and making calculations. Artificial Intelligence, on the other hand, is about replacing human decision-making with more sophisticated technologies—AI is built to learn continually and improve over time. To unlock the value of AI algorithms, companies need access to large data sets, must apply data processing power, and interpret results strategically.
AI handles three types of data exceptionally well:
- parameters and numbers, generating insights beyond human accuracy
- analyzing, interpreting, and writing text, using context-aware natural language processing with AI, with near-human accuracy
- images (spotting patterns, object/human/face recognition, scene understanding, activity detection, and automatic equipment audit/inspection beyond human accuracy), using deep learning methods for computer vision.
Filtering information and analyzing sentiment
AI helps humans work more effectively by filtering key information from a wide variety of sources. For example, AlphaSense‘s sophisticated search functionality leverages natural language processing to find and track relevant information in search, learning from successes and mistakes with each search. Reuters News Tracer filters tweets through Machine Learning algorithms to pick up on breaking news before it’s reported elsewhere.
Likewise, financial services companies can use AI to detect brand sentiment from social media and text data, measure it, and transform it into actionable advice. Sentiment analysis assists with advanced classification of textual data (e.g., for compliance). These would be relatively novel applications of artificial intelligence, particularly in the arena of finance.
Trading: Better trading through algorithms
AI can help manage and augment rules and trading decisions, helping process the data and creating the algorithms managing trading rules. Investment firms have implemented trading algorithms based on sentiment and insights from social media and other public data sources for years.
Hong Kong-based Aidya uses algorithms to conduct trades autonomously, and some companies, like Japan-based Nomura Securities, relies on AI robo-traders for high-frequency trading, to boost profits.
Investing: Fintech companies offer investment insights
In the wealth management arena, B2C robo-advisors augment portfolio management and rebalancing decisions made by humans, often analyzing a person’s portfolio, risk tolerance, and previous investment decisions to offer advice. Kensho‘s intelligence-grade database provides traders with information on market trends around the globe, and ForwardLane provides financial advisors with personalized investment advice and quantitative modeling that used to only be available to extremely wealthy clients, using AI powered by IBM’s Watson. As mentioned earlier, Wealthfront uses AI to track account activity and help financial advisors customize the guidance they give investors.
Banking: AI enhances efficiency, offers data insights, and manages risk
Chatbots help banks serve customers more efficiently, even though they aren’t advanced enough to handle support cases autonomously. Powered by natural language processing, bots can listen in on agents’ calls, provide accurate answers quickly, and suggest best practice answers to improve sales effectiveness. Neural networks help agents respond to common customer service queries by sorting and labeling metadata and generating three potential responses, each with a level of certainty attached.
As we mentioned earlier, we’re already seeing banks like Wells Fargo using chatbots to improve the customer experience and reduce time and cost. Many of these virtual assistants use predictive analytics and cognitive technologies to personalize customer support, accessing a user’s financial portfolio, banking history, and goals, to automate trades and give advice. Predictive analytics are able to leverage a company’s customer base for churn prediction, advanced revenue prediction, and sales forecasting.
Financial firms take advantage of AI to identify the clients most likely to leave a bank or advisor. Finn.ai‘s white label chatbot integrates into existing messaging platforms, as well as a bank’s web chat interface.
If a financial firm’s data is unstructured, or the company has many databases that store information about entities separately, it’s difficult to link and connect information. An army of human analysts used to be required for such projects, but now, it can be done via AI, with minor human supervision.
Lending: AI for credit lending
Machine Learning is a game-changing technology for lenders, lowering compliance and regulatory costs and helping with robust credit scoring and lending applications. Credit decisionmakers can use AI for robust credit lending applications to achieve faster, more accurate risk assessment, using machine intelligence to factor in the character and capacity of applicants. For example, underwrite.ai applies advances in AI derived from genomics and particle physics to provide lenders with nonlinear, dynamic models of credit risk which radically outperform traditional approaches. This can supplement young adults’ and self-employed professionals’ often thin credit history. In fact, FICO uses AI, to build credit risk models. AI can also help creditors collect outstanding debts, by using Machine Learning to generate insights that are hard for humans to spot.
Underwrite.ai – Machine Learning + Big Data for Credit Underwriting
Fraud detection
A 2015 study by the research firm Javelin Strategy found that false positives—legitimate transactions that are wrongly rejected, due to suspected fraud—account for $118 billions of dollars in annual losses for retailers, not to mention lost customers, who will often abandon the issuer of the erroneous decline. Machine Learning algorithms, like those used by Mastercard’s Decision Intelligence technology analyze various data points to identify fraudulent transactions that human analysts might miss, while improving real-time approval accuracy and reducing false declines. Using Machine Learning to spot unusual patterns and improve general regulatory compliance workflows helps financial organizations be more efficient and accurate in their processes.
Image Recognition in FinTech
As we mentioned earlier, when we talked about JPMorgan Chase’s CoiN platform, recent advances in deep learning have increased image recognition accuracy to levels that surpass that of humans. Cofirm.io automatically authenticates consumer identity documents, and Onfido’s platform plugs into various publicly available databases to give employers quick identity verification and background checks for things like driving and criminal records.
Banks can use AI technology to stay in compliance and identify fraud. For example, IPSoft’s Amelia uses Natural Language Processing to scan legal and regulatory text for compliance issues.
Artificial intelligence helps financial services companies make money by enhancing the accuracy of trading and by making wealth management more efficient.
Main Takeaways
Artificial Intelligence has countless applications in the financial services ecosystem that are poised to transform the industry in the next several years, including detecting and analyzing brand sentiment; providing investment insights; making banking more efficient and less risky, and identifying fraud.