Q&A: Farron Blanc, Legal & General, on developing data and processes

May 31, 2023 5:35 AM

Digital Insurance spoke with Farron Blanc, vice president, brokerage distribution and strategy, Legal & General America (LGA). The U.K.-based insurer, which emphasizes term life coverage, has a significant U.S. business. LGA has invested in its insurance brokerage distribution and its work with brokerage general agencies (BGAs), offering its Partner Dashboard service for them to use with their clients. In June, LGA is preparing to launch a "term to term" exchange in which insured can exchange their coverage product for one that better fits their current needs. Blanc joined LGA 18 months ago after experiences starting an innovation unit at Reinsurance Group of America and co-founding and leading Gerry, a senior living services start-up.

How is Legal & General America innovating in its work with brokerage general agencies?

Farron Blanc, vice president, brokerage distribution and strategy, Legal and General.

We're trying to meet any advisor where they are. One of our large BGA partners, owned by a bank with hundreds of millions of dollars of revenue, has a dedicated transaction center. Every producer contracted through that BGA has to go through that dedicated transaction center to handle that process. In that scenario, we build an API, test it, make sure it works and it's humming.

On the other extreme, for a third- or fourth-generation family business with five people in an office supporting a couple hundred advisors, we have to invest in our own technology, our own website, that's easy for them to use. From API to user interface, the full suite. We had to really build and understand and do human-centered design, sitting down, understanding who's using it and when.

Last year, we iterated, improved and continued to fix the process and the platform. We reached out to the case managers and the illustration specialists -- the people who are actually using the platform – getting their feedback day in, day out. Every two weeks we have a release or an update. Every four weeks we have a major update, where we're just trying to figure out the use cases. What are the pain points looking at the data, to reduce emails that are coming in? When a BGA reaches out by email, typically it's because the process isn't what they expected. So how can we remove the need for them to ever send us an email? That's what we've invested in, and then using data to identify areas to remove friction in the consumer process.

What is Legal & General America streamlining in its operations work?

Attending physician statements [APSs] are the bane of anyone you talk to in life insurance. They dislike it because it takes so long and they're relatively expensive. It could take anywhere from three weeks to three months. They're high variability.

The flip side is huge protective value. Being able to look through a longitudinal attending physician statement over years, has huge impacts, or provides a lot of value when trying to underwrite mortality. We've invested a lot in alternative data sources. We use electronic health records from labs as well as urgent care walk-in clinics. We invest in assessing and standardizing pharmaceutical records, pharmaceutical drugs and motor vehicle records.

APSs are the gold standard in mortality prediction. With all those new datasets that we've been investing in, the second thing we've done is to order all the evidence at the same time. Depending what one comes back first, we might cancel an outstanding request.

For us, every day matters and for our partners, for our BGAs and for our clients, every day matters. We can't sit around and wait two weeks and say, "I've taken two weeks for the APS to come in. Now let's get an EHR [electronic health record]." We do that all on day one, automatically through software that we've developed, to manage 1,000 applications a day.

Are there alternative data sources or knowledge that Legal & General America uses?

We have a research and development team that's constantly looking at new sources of data. We're looking at genomic genetic data now but we are not using it in production. We're looking at medical claims data and different sources of data, like dental records, to see if the're worth adding. We're investing a lot of effort in seeing if we can extract more insights out of the existing data we get.

For that, we're investing heavily in summarization services and tools and workflows. An APS might be 1,000 pages. You don't want an underwriter reading 1,000 pages, when on page 79, there's one comment that the underwriter can piece together with a lab result to decide whether to decline the policy, postpone it or add an extra provision. Sifting through that 15 times a day is another challenge. We're investing in machine learning and natural language processing to summarize that and then surface it to the underwriter. Only surfacing the cases that make sense.

What goals for improving on technology could be next?

We've been talking a lot about underwriting. If you can underwrite effectively, that enables a company to protect more lives and do it responsibly, and get the most affordable product price and do it sustainably right. There is a huge, huge area that our industry hasn't been great at, traditionally, which is raising that awareness and speaking to people where they are, when they are and and explaining clearly why they should consider life insurance.

We're constantly thinking of how to bring better personalized marketing awareness, stories and conversations to our producers, our BGAs and consumers, or giving tools for our BGAs to have those conversations one on one with producers at an efficient cost. We have 100,000 producers -- advisors that sell or are contracted to sell. They'll often say they can't make enough money selling life insurance, given their time.

We're thinking about how to bring some of those skills for AI/ML, OCR [optical character recognition] and more to the underwriter. Also about how we bring that to the producer so that they can have better conversations and more cost effectively reach underserved populations, like young people, other cultures, societies or demographic segments that the industry typically hasn't done a great job with, because of the economics and the current delivery model.