London Market Data Pain – Common But Not Compulsory…
It was on one rainy London day, back in 2017, when it all came out. We were showing an early version of our software and for one Operations Manager it was all too much.
“We’re checking our inbound risk data manually, using clunky exception reports on our policy systems….we send out the reports by email but nobody looks at them. There’s no accountability with business users and the process is so opaque we can’t see if things are being fixed. This data is so critical there’s no excuse for us not getting this right.”
He thumped the table with his hand: “What we need is to know about this stuff early…it should be intuitive and embedded as part of our everyday process…..it should be like breathing!”
This response chimed with what we already knew from our five years consulting in and around the specialty insurance market. Poor data checks were resulting in bad data – causing pain across all departments of London Market carriers on a daily basis. Worse, it was costing them time and money. Two resources that carriers, in one of the worst markets in years, can ill afford to lose.
It starts with Operations….
In our experience the pain is first felt at the point of ingestion, typically in Underwriting Ops. Ops teams, whether in-house or outsourced, are often on the hook for data entry but the sheer variability and complexity of data makes their job a challenging one. Typically this requires employing human headcount to check data more thoroughly or to apply only limited checks – we call it “winging it” with the hope that when data issues leak through they can be managed in time when, and indeed if, they are spotted.
Working with underwriters and brokers to resolve issues is time consuming and currently happens off-line, via email, phone or in person. The whole process can leave team members ‘data fatigued’, resulting in low morale, a lack of any audit trail and operational employees who feel they are not fulfilling their true potential.
…..and hits every other area of Carrier Operations
Lack of comprehensive checking upstream results in a domino effect as numerous secondary issues are created downstream, each carrying their own cost in wasted employee time and effort. Here are some of the most common impacts we’ve observed:
- Actuaries spend days cleaning data prior to starting their capital modelling process or in preparing their regulatory submissions.
- Compliance departments, often forced to run limited checks themselves, fail to pick up breaches in controls before they cause serious operational and reputational damage.
- Claims teams find discrepancies between their policy systems and the latest market loss position. Or worse, oddities like closed claims still with significant outstanding reserves tying up capital.
- Exposure Management teams struggle to identify and locate missing property data from schedules of values that make a material difference to potential loss, and accurate reinsurance purchase.
- Finance experience data quality pain associated with the monthly, quarterly and GAAP close process. Not to mention huge issues with calculating taxes and broker commissions correctly.
- Data Governance teams have the theory and good intentions but struggle to engage and build partnerships with business teams.
- Audit teams spend several confidence , sapping weeks gathering the evidence and transactional information they need to check due process has been followed.
Getting to grips with this problem has always been daunting. “It’s like solving world hunger….” said one COO, “we’d all like to fix it, but the problem is so endemic and widespread it’s hard to know where to start.”
Other carriers are too busy fighting other fires that they’ve all but given up – so much so that just “getting on with it” has become a less painful option. In fact, this is one of the biggest reasons why dealing with poorly checked data has long become normalised into the expense ratio of carrier operations.
Yet with up to 2% of a carriers expense ratio allocated to processing data, it presents a fantastic opportunity for those of us on a mission to solve it. As we look to 2020, carriers are looking for smart ways to quickly cut costs , improve compliance and establish the baseline data confidence they need for a digital future. Regulators demand data hygiene more than ever, and to harness the current wave of insurtech, with it’s promise of an AI driven future, it’s all but essential.
How do you Measure Up? 5 Signs you have Data Pain….
So how does your specialty carrier, MGA or broker stack up when it comes to checking data? Are you winging it? Or dealing with the pain in a limited, inefficient manner?
Here are 5 tell tale signs you have data pain (but also significant efficiency potential):
- You employ or outsource a dedicated team of human FTE’s to perform routine QA checks on risks
- You adopt a “sampling” approach – checking only a percentage of inbound slips, policies and claims
- You have a legacy suite of exception reports but can’t say who uses most of them or why
- You frequently have to correct key data the day before month end, GAAP close or submission of a regulatory report
- You have data trust issues. When the chips are down, you can’t say with real authority that you are 100% confident in the operational data that matters. You’re guessing, hoping or winging it…
What’s the answer?
Clearly, traditional approaches haven’t worked to date. Denial, well intentioned in-house solutions, or expensive technical DQ tools – we’ve seen all of these approaches try and fail over the years.
We’re on a mission to heal the pain and our vision is simple – to give Specialty carriers , MGAs and brokers continuous data confidence, anytime, anywhere. In fact, DQPro is now helping many of the worlds leading specialty carriers solve this painpoint, at scale and across multiple systems and branches. We’re global too with over 150,000+ checks run for regular business users, saving thousand of man hours across 5 continents to date.
Then get in touch for a chat, a demo or just to meet one of our many happy customers. We’d love to show you how…..
Ps. Did you notice we didn’t say the dreaded words “data governance” once?