John and Jane Smith walk into a mortgage network office and tell the loan officer they are interested in a loan for a house they are hoping to buy.
The loan officer is excited about the opportunity to work with them and promptly pulls out the list of needed information to process their loan application.
The Smiths review the list of needed information and tell the loan officer, “We dont want to tell you anything about our existing home other than it is really nice and we think it is worth $250,000. By the way, we have tax returns from last year, but we changed accountants, so any older information would be too difficult to get.”
The loan officer tries his best to convince the Smiths of the need for the older returns. The Smiths interrupt and say, “We have three other banks willing to give us loans with the information weve described. Do you want our business or not?”
It seems as if every time I attend a group disability insurance industry meeting or visit an underwriting department, one topic of discussion is the quality of data received on prospects. Or more appropriately, the lack of quality data received on prospects. The problem isnt only associated with large cases, but that certainly is where the data issues are most pronounced. As the disability industry has struggled to attain desired profit levels, we must continue to address this problem.
For disability insurance, the data quality problem cuts across all aspects of a case analysis. Census files often are missing dates of birth or other key fields, claims information is incomplete and a copy of the current plan may not be included. Underwriters cant do an appropriate review without this information. An underwriter can often make a reasonable assumption about one or two aspects of a case, but more and more, it seems we are faced with multiple pieces of missing data.
One area of concern related to this is that newer underwriters may not recognize the apparent inconsistencies in the data. The newer underwriters might accept what has been submitted as complete. I have seen several situations lately where the claim listing is a year old, and we are told that there have not been any new claims in the last 12 months. That is hard to believe on a 3,500-life hospital that should generate 15 to 20 claims a year. In fact, when we pursue the claim information, we receive an updated listing with a current date.
The data we need to properly assess an LTD risk has not changed significantly over the last 15 years. The industry has made incredible progress in the technology we use to manipulate and analyze the data we receive.
The question is: Are we any further ahead if we are putting worse data into better tools? Some of the tools being used by brokers and carriers to prepare RFPs and RFP responses hold great potential as long as we dont sacrifice needed data for simplification and speed of the bid process.
Carriers also have made significant investments in national accounts or large case areas to help with the RFP process. My sense is that we have focused more attention on the qualitative and service-related responses and less attention on the quantitative data related analysis.
The following is a list of our desired quote information:
1. Full name and complete address of policyholder.
2. Specific nature of the business (4-digit SIC, if known).
3. Current plan design, any plan changes in the past three years and any requested alternatives.
Copy of policy or certificate.
Definition of eligibility including the number of eligible employees and a clear definition of any classes especially if multiple classes are needed for rating or benefit differentiation.
4. Current census including gender, birth date or age, salary, occupation and class.
5. Request for proposal documents.
6. Special case issues, such as competition and broker concerns.
On LTD cases over 250 lives, we will need to experience rate. Therefore, we will require the following information:
1. Paid premium for the prior three years.
2. Paid claims for the prior three years.