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Financial Services

Optimize Your Call Center and Financial Advice Methods with AI

call center

Previously, I discussed artificial intelligence (AI) and the benefits it can have when transforming retirement services. In this next post, I look at call center optimization and how AI provides an in-depth look into financial advice.

Call Center Optimization

Call centers are a necessary evil for financial services organizations, and none feel the pain more acutely than retirement services organizations. With a business model that focuses on supporting an aging (and therefore potentially less technology-savvy) population, and where arcane and convoluted rules (e.g., hardship withdrawals, loan availability, vesting, match rules, ADP/ACP testing) need to be explained effectively, call centers can become very resource-intensive and expensive to operate.

With the advent of AI, there are several ways these challenges can be addressed. A few more obvious examples include:

  • Escalating calls immediately to managers when a client has a history of having problems with their account
  • Automatically presenting data to a representative when a call is received based on previous interactions with the client (e.g., if they were recently issued a loan, having that information already populated for the representative when they receive the call)
  • Automatically populating data for a representative during a call, making the conversations quicker and more comfortable for the customer
  • Presenting the “next-best option” in real time to help reps predict what clients might be interested in based on the caller’s history

In late summer and early fall, most retirement services call centers start to experience a spike in calls. These are usually driven by people who are looking into hardship withdrawal information for post-secondary education. Most retirement services call centers would rather have their representative’s focus on “higher-value” transactions that require more personalized interactions, rather than walking a client through the basics of hardship withdrawal rules.

Artificial intelligence can be used to drive chatbots that understand what the client is asking and then explain to them what their options are. Chatbots can also identify when pivotal moments in the conversation have been reached. For example, when emotions are running high, the client can be connected with a representative. Or, if the client needs even more personalized information, the call can be routed accordingly.

Financial Advice

With such a dizzying number of options available, one of the challenges that many financial services clients now face is how to invest their money. This was touched upon in our guide, Conquering Selection Analysis Paralysis in Financial Services, in which we discussed ways that technology can help overcome fears and regrets that may accompany decisions in choosing investment options.

AI in the form of chatbots can review accessible client data (e.g., age, income) and can interact with the client to determine additional required information (e.g., level of risk tolerance). These interactive tools can be, and often are, effective mechanisms to help clients with financial decisions without having to employ licensed financial advisors to support their call centers.

But if the desire is to take a more proactive approach to help clients with their investment challenges, there is no reason why ML can’t also be used.

Though there are many reasons a client might make decisions with their investments, it is possible to use ML to review accounts for indicators where investments don’t seem to be in line with client needs and then prompt a communication to the client from a financial advisor.

Examples of these rules include:

  • Clients who are rapidly approaching retirement and are invested in high-risk funds
  • Clients who are rapidly approaching retirement and are invested in funds that won’t mature until much later
  • Clients who are younger and are entirely focused on bonds and stable-value funds

By capitalizing on an investment in AI and ML, service providers can offer financial advice without having to employ an army of licensed advisors.

To learn more about the opportunities to transform a call center or methods to provide the best financial advice with artificial intelligence, you can click here, or download the guide below.

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Andrew Bihl

Retirement Services Lead, Financial Services, Perficient Andrew Bihl, retirement services lead in Perficient’s financial services practice, joined the company in 2013 via the acquisition of ForwardThink Group. His areas of focus include program management offices, business process redesigns, and operational design. Andrew has over 20 years of experience in corporate and consulting roles. He has successfully delivered projects in the retirement services practice for diverse clients, such as Fidelity, Prudential, and TIAA.

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