What the Board Should Demand Before Tolerating Broker Submissions That Cannot Be Compared
Board-Level Governance for Submission Data Standardisation
The board should demand, before tolerating broker submissions that cannot be compared, that management demonstrate the controls, metrics, and investment plan that govern the submission-data quality. Specifically, the board should demand: the proportion of new business sourced from standard versus non-standard submissions, the profitability drag attributable to the non-standard submissions, the defined data-quality target, the operating controls that enforce the data standard, the investment case for the submission-standardisation platform, and the timeline for achieving the target. For non-executive directors and risk committee chairs, the board's tolerance of incomparable submissions is a governance decision: the board that does not demand the controls is tolerating a data-quality deficiency that directly affects the portfolio's risk-selection integrity, and the board that demands the controls governs the data quality on which the enterprise's underwriting decisions depend.
Why does the board-level demand for submission-data governance matter more now?
The board-level demand matters more now because the portfolio's growth is increasing the volume of submissions, and the data-quality problem is scaling with the growth. A board that does not demand the controls when the portfolio is small may find the data-quality problem compounded when the portfolio has grown, and the board's governance is then retrospective—demanding the controls after the problem has accumulated.
The second reason is the regulatory expectation that the board governs the quality of the data the enterprise uses for risk decisions. A regulator reviewing the board's governance framework will expect the board to have verified that the underwriting data is consistent and comparable, and a board that cannot demonstrate that verification has a governance gap. The enterprise risk framework depends on the board's governance of the data quality.
The third reason is the board's fiduciary responsibility to ensure the portfolio's risk-selection is based on comparable data. If the portfolio's composition is skewed by the incomparability of submissions—business sourced from brokers whose submissions are easier to evaluate, not brokers whose risks are better—the board is governing a portfolio whose risk the data inconsistency has distorted, and the board's fiduciary governance is weakened.
What goes wrong when the board does not demand the controls?
When the board does not demand the controls, management does not prioritise the investment, the submission-data quality remains ungoverned, the portfolio's risk-selection is based on incomparable data, the CUO's governance is unsupported by the tools the team needs, and the board's oversight is based on an assumption of data quality that has not been verified.
Demand the submission-data governance controls before the portfolio's growth compounds the data-quality problem
Visit Insurnest to learn how we help boards build the governance framework for broker-submission data quality.
What do board members and risk committee chairs actually need from the data-quality governance?
Board members need management to present: the data-quality metrics, the profitability impact, the controls inventory, the investment case, the target, and the timeline.
Vikrant is the chair of the risk committee. During a review, he asked the CUO what proportion of submissions were in a standard, comparable format. The CUO estimated forty percent. Vikrant asked: what is the profitability impact of the sixty percent that are not comparable, and what is the plan to standardise them? The CUO could not answer. Vikrant directed management to produce the data-quality analysis, build the investment case, and present it to the committee within ninety days.
That is what every board should be demanding: the data-quality governance that the portfolio's risk-selection integrity requires.
- A management presentation of the submission-data quality metrics to the risk committee quarterly. "The proportion of standard submissions, the margin drag from non-standard submissions, and the broker data-quality scores."
- An investment case for the submission-standardisation platform, presented to the board. "The platform cost, the margin preserved, the ROI, and the implementation timeline."
- A board-approved data-quality target, included in the CUO's performance objectives. "Eighty percent of new business from standard submissions within twenty-four months."
- An internal audit review of the submission-data management process, commissioned by the audit committee.
- A board-level question: can management demonstrate that the portfolio's risk-selection is based on comparable data? "Asked quarterly, with the CUO's response minuted."
- A regulatory-readiness demonstration that the board governs the submission-data quality.
- An annual review by the board of the data-quality governance progress.
- A broker-communication strategy that signals the board's expectation of data quality.
How can boards build the submission-data governance?
By adding the data-quality question to the risk committee's quarterly agenda, directing management to produce the metrics and the investment case, commissioning internal audit to review the process, and setting the target.
What does the board's demand deliver in practice?
A management team that prioritises the submission-data quality, an investment that is sponsored and approved, and a board that governs the data quality on which the portfolio's risk decisions depend.
The broader governance reflection is that the board governs the enterprise's risk through the data it receives, and a board that does not govern the quality of that data governs the risk on a data foundation whose quality it has assumed. The board's demand for submission-data governance is the governance mechanism that verifies the assumption.
Conclusion
For non-executive directors and risk committee chairs, the board's demand for submission-data governance is the governance mechanism that ensures the portfolio's risk-selection decisions are based on comparable data. The board that demands the metrics, the controls, the investment case, and the target governs the data quality on which the enterprise's risk governance depends.
Frequently asked questions
What should the board demand before tolerating incomparable submissions?
The proportion of standard-submission business, the profitability impact, the investment case for standardisation, and the timeline for achieving the data-quality target.
How does the board verify submission-data quality is governed?
By asking the CUO and CFO to present data-quality metrics quarterly, and by directing internal audit to review the submission-management process.
What is the governance consequence of tolerating incomparable submissions without controls?
The board governs on the assumption that risk-selection is based on comparable data, and if that assumption is incorrect, the board's risk governance is incomplete.
What metric should the board track?
The proportion of standard-submission business, the margin drag from non-standard submissions, and broker data-quality scores.
How does the board ensure the data standard is enforced?
By including the data-quality target in the CUO's performance objectives and reviewing the metric quarterly.
What question should the risk committee ask?
Can management demonstrate that risk-selection decisions are based on comparable data, and what controls ensure submission data is complete and consistent?
How does the board's demand affect the broker market?
It signals through management that data quality is a governed board-level parameter, raising the standard across broker relationships.
What investment should the board expect management to propose?
The submission-standardisation platform with an investment case based on the margin the standardisation will preserve.
About the author
Hitul Mistry is the Founder of Insurnest, an InsurTech company that engineers end-to-end technology exclusively for the insurance industry serving carriers, TPAs, MGAs, brokers, and reinsurers across India, the UAE, and the US. With more than a decade of insurance domain experience, he has built systems spanning underwriting automation, AI-powered underwriting intelligence, claims management, rating and quoting, broking and agency platforms, and reinsurance automation across Health/GMC, Group Life, Motor, P&C, and Reinsurance. Insurnest doesn't adapt generic software to insurance; it builds from the workflow up.
Connect with Hitul on LinkedIn.
