The financial sector is the cornerstone of every market. Banks, venture capital firms, and fintech companies help fuel other industries by providing key services that help businesses protect their assets and grow.
Unfortunately, it’s also an incredibly complex industry, plagued with challenges that seem to come from every corner. Regulatory constraints offer protection, but often slow down innovation. High costs associated with errors place an enormous amount of financial risk on companies when making key business decisions. In many cases, those costs are then passed down to their customers.
While these complexities are not easily solved, there has been a wave of technologies seeking to help tackle the problems facing the financial industry. These solutions are aiming to improve not only the experience for end users, but also the business performance of companies, government agencies, and experts in this space. Some of the most promising are AI and machine learning technologies geared toward helping improve financial services.
A PwC study found that around 66% of financial services professionals haven’t engaged with AI solutions yet. Most attributed this to cost, operational, and regulatory barriers. While these applications haven’t been universally adopted, we are optimistic about the future of the technology and its applications in the financial sector.
One of the biggest challenges in the financial and banking field is the complexity involved in identifying fraud. By leveraging better AI platforms and solutions, however, companies can automate the analysis of massive amounts of data. This means fraudulent actors and other threats can be recognized and dealt with faster.
Know Your Customer
For example, when it comes to Know Your Customer processes, manually assessing customer credentials takes time. In most cases, these processes still depend on a certain degree of human judgment.
When you leverage AI to automate Know Your Customer processes for wealth management and investment teams, it is possible to take a process that takes hours or one that often results in human error, to an automated process that reduces instances of fraud in a fraction of the time.
Most of the time when you hear about AI for financial services people talk about robo-advisors. They rarely take into account how AI can offer decision support for human experts. Anand Rao, US Analytics Group Innovation Leader, said it well, “Artificial intelligence can help people make faster, better, and cheaper decisions. But you have to be willing to collaborate with the machine, and not just treat it as either a servant or an overlord.”
Advanced AI and machine learning algorithms can help identify issues that would take human experts hours or even days to recognize. For example, when reviewing a potential investment, these tools can help recognize regulatory compliance issues or subtle patterns that may indicate problems down the line. By flagging investments that may be risks, AI can support human decision making while keep financial experts investing their time in tasks that require human judgment.
Changing Contract and Document Review
AI has the potential to help financial firms manage contracts better, a task which usually requires enormous legal teams. JP Morgan recently announced that they deployed a new AI system for contract management. Explained in their annual report, the platform “uses unsupervised machine learning to analyze legal documents and to extract important data points and clauses” As more companies recognize the importance of partnering human experts with machine intelligence, they will turn to experts in AI who can help develop bespoke AI solutions.
Outside of contract review, AI can be deployed to crawl, aggregate, analyze, and visualize massive amounts of data from web and enterprise sources, aggregating them according to specific use cases, analyzing them for patterns, relations, and entities. This could mean firms can identify business opportunities faster, by gaining access to unstructured data that more traditional queries wouldn’t be able to find.
Descriptive, prescriptive and predictive analytics enable insights into past performance of markets and the future market outlook of services a firm may be considering. By giving practical insights on the potential of a new service, AI can help financial services companies better forecast the success of a rollout.
The Path Forward
In parallel industries, increased access to data and AI has optimized industry performance while improving customer experiences. To achieve the same results and to continue the pace of innovation, financial companies and tech firms will need to work together to develop better solutions to solve the complex problems the financial industry faces.
It’s my belief that when professionals are empowered with better insights, innovation will happen at a much more rapid pace. At Innoplexus we are working to shape the third wave of AI that will not only help normalize problems in the context of a domain but also go a step ahead in reasoning. This third wave of AI has the potential to free up human potential, currently tied down in mundane tasks, to help shape the future of the Financial Services Industry.
Gunjan Bhardwaj is the Founder and CEO of the Innoplexus group. Innoplexus is a leader in AI, machine learning, and analytics as a service for healthcare, pharma, life sciences, and financial services.He was earlier with the Boston Consulting Group and before that the leader of the global business performance think-tank of Ernst & Young and a manager in the German practice with a solution focus on strategy and innovation.