In the last few years, those in the finance and banking industry have started to see the power of bringing machine learning to their industry. Now financial institutions of all sizes are in a scramble to not only reduce bureaucracy but also increase the accuracy and fairness of the lending process.
Unfortunately, many financial institutions are still using a heuristic approach to manage complex, highly-regulated processes — this is to say, the rules and regulations of their processes are manually configured and updated using an army of (expensive) legal, financial, and computer programming experts.
What’s Driving the Urgency to Adopt AI?
A need for increased speed and efficiency • The opportunity for deeper data-driven insights • Complex regulations and compliance requirements • Poor customer service and long wait times. • Credit scores not telling the whole story.
When Machine Learning Comes to Finance and Banking
- Novel AI-powered processes and products generate new revenue streams.
- Multidimensional and cross-jurisdictional fraud schemes are detected instantly.
- Customer acquisition becomes more personalized, streamlined, and data-driven.
- Financial reports, compliance assurances, and administrative tasks are completed automatically.
- Risk assessment and underwriting goes beyond credit scores.
- Claims are validated and losses determined via image processing.
Key Uses for AI in the Finance and Banking Sector
Text Organization and Summarization
Manceps can help financial institutions apply natural language processing to large volumes of text and speech data to extract information, gain insights, and streamline manual tasks. While time and cost savings are obvious benefits, the ability to identify key information (the proverbial needle in the haystack) can make all the difference. Consider bringing automated summarization to legal documents, earnings reports, or job applications.
Fraud detection now involves more than a checklist of risk factors. Using ML techniques, fraud detection systems can now actively learn and calibrate in response to new (or potential) security threats. By analyzing billions of data points, these systems can flag issues that would otherwise go unnoticed by humans, preventing false rejections along the way.
Personalized Retail Experiences
Customers are becoming increasingly adept at using Chatbots and other conversational interfaces for their banking needs. Such chatbots have to be built using robust natural language processing engines as well as mountains of finance-specific customer interactions. These technologies make it increasingly difficult for bank customers to tell whether they are actually speaking to a human.
Chatbots, financial assistants, and related tools are self-learning, which means they increasingly improve with additional customer interactions.
Predictive analytics run on artificial intelligence, and in the financial sector, the introduction of such insights can drive revenue and reduce costs. Financial organizations have used predictive analytics for a variety of purposes. It’s allowed them to identify and target more profitable customers; better manage cashflow; anticipate demand fluctuations, and mitigate risk. As AI becomes increasingly capable, financial organizations are looking to find ever-complex ways to put that data to work.
Like many large organizations, financial institutions are turning to artificial intelligence to automate oft-repeated tasks and to help their business run more smoothly. In one example, JPMorgan used bots to help employees reset their passwords. They expected that almost 2 million requests would be handled by the bots in 2017, about the work of 40 full-time employees.
This article was adapted from Manceps’ Finance and Banking services page. Manceps helps organizations not only build and deploy AI models at scale but also identify the business use cases that make an investment in AI possible.
If you’re thinking about bringing AI to your organization, I recommend that you start with this excellent resource: Discussion Questions for AI Readiness.
For more use cases related to finance, consider checking out: AI Examples from the World’s Biggest Finance and Banking Companies.