Imagine yourself seated between two people at a business dinner. The man to your left stockpiles factoids about fishing outings throughout history — number of fish caught, their lengths and weight, date, time of day, weather and water conditions. Setting aside the usefulness of all this data, there’s little doubt that he has a lot of it.
The woman on your right, on the other hand, knows the great fishing spots within a 50-mile radius, and better yet, she knows which of them are best for fly-fishing, which you happen to prefer.
Both dinner companions’ knowledge can lead you to pick the optimal spot for your next trip, but the first person’s data-driven approach could return a glut of results that don’t fit your needs. What he lacks, and what your other companion has, is a bit of information about you — your desire to stay close by and your preference for fly fishing — that would cull the list of potential locations from millions across the globe to a handful in one small corner of the world.
Today’s artificial intelligence solutions for the wealth management space aim to assist their users like the data-loaded dinner guest. While these systems leverage tons of data and incredible speed, they lack what the woman offers: context. Without context, AI tools cannot fully grasp user intent and are limited in their ability to create significant efficiencies or reduce the need for human operational support.
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The complex nature of the wealth management sector calls for a different approach to AI; one that takes advantage of the rules and regulations that complicate the industry’s operations.
An AI-enabled platform that uses specific operational, administrative, strategic and compliance concepts of a firm and industry, and importantly, draws connections between those concepts and predicts likely future requests provides a significantly better system for the financial services industry, than machine learning algorithms that rely solely on past data of interactions.
This concept is known as a knowledge graph. It allows algorithms to focus on a relatively narrow set of related topics and interpret what the user wants, returning a more personalized and actionable set of results, rather than having to mine a much larger data set.
For example, knowledge graph enabled AI algorithm will know that if a financial advisor’s client who only has retirement accounts at her firm requests a rollover, it should search only data sets related to IRAs, employer retirement plans and applicable policies, rather than running through a data set that includes most frequently asked questions about life insurance, 529 plans and a slew of other topics to provide a scripted response or a link to several data sources.
Today, completing rote processes occupy about 40 percent of a financial services professional’s productive time. Technology decision makers often implement productivity solutions to reduce the burden of these processes, but the scatter-shot approach to sooth these pain points often causes additional problems for users. When applications support isolated sets of information, it diminishes enterprise efficiency.
Vertically focused solutions well-versed in the operational details of specific financial services business models, including each model’s rules, requirements and regulatory obligations, can create online experiences that feels like a user received assistance from a colleague rather than a computer.
Because of the complexity of current workflow systems, financial institutions spend over 30 percent of their revenue in administrative and user support operations, along with the compliance efforts associated with those functions. For financial services firms looking to expand, these costs can be prohibitive.
When growth-minded firms consider technology and AI-enabled solutions, they must not only address costs but how these tools enable them to transform critical processes without disrupting operations. These firms need a tool that allows them to drive growth without sacrificing support for professionals through transition periods.
A well-integrated, knowledge graph-supported system can drive efficiencies, eliminate lost revenues and reduce costs. More importantly, these systems can also support professional retention and engagement during expansion. When a firm makes that choice to grow aggressively, it runs the risk of alienating legacy members of its team, and by providing a solution that provides actionable results and reduces the burden on administrative staff, everyone wins.
Some who question the value of integrating contextual cues such as knowledge graphs to help AI-based tools work more efficiently and effectively in all situations. And they may have a point.
However, when applied to such a smaller subset of information, such as the case when applied to enterprise systems supporting wealth management firms, this kind of upfront investment in technology serves to diminish redundancies and costs while delivering significant return almost immediately.
Dr. Sindhu Joseph is the founder and CEO of CogniCor, an AI-powered digital assistant platform for financial firms. She earned a Ph.D. in AI and holds six patents, and is an author and speaker on topics around enterprise AI and the need for diversity across the tech industry.
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