James Taylor of Decision Management Solutions and Deb Smallwood of Strategy Meets Action present he Top 10 Imperatives for Insurers and the role of Decision Management in addressing them. Webinar
James Taylor of Decision Management Solutions and Deb Smallwood of Strategy Meets Action present he Top 10 Imperatives for Insurers and the role of Decision Management in addressing them. Webinar recording available at http://decisionmanagement.omnovia.com/archives/64033
As Insurers position for market recovery and deal with the forces reshaping the insurance business, SMA’s industry research has identified 10 critical imperatives that will deliver sustainable profitable growth, agile customer service and scalable business optimization. At the core of these initiatives is putting your data to work, and putting it to work across channels in day-to-day, front-line operations. This webinar will:Introduce the top 10 imperativesShow how analytics and a roadmap to a modern IT architecture are essentialIntroduce decision management as a framework for applying analytics in operational systemsIdentify next steps for insurers
The day to day decisions that drive operational behavior, customer interactions, transactional systems are more important than the big, strategic decisions beloved of management consultants. Each one seems unimportant but they happen so often that their total value swamps anything else you do. If you get these decisions wrong it won’t matter what you get right.
At its heart a decision is a choice, a selection of a course of action. A decision is arrived at after consideration and it ends uncertainty or dispute about something.Decisions are made only after considering various facts or pieces of information about the situation and participants.Decisions select from alternatives, typically to find the one most profitable or appropriate for an organization.Decisions result in an action being taken, not just knowledge being added to what’s knownThe basic decision making process is simple. Data is gathered on which to base the decision. Some analysis of this data is performed and rules derived from company policy, regulations, best practices and experience is applied. A course of action, a selection from the possible options, is then made so that it can be acted on. When considering decisions in operational business processes, the way the decision is made is often constrained such that it can be described and automated effectively in many, even most, cases.
As we are talking about decisions it is worth remembering that all decisions matter, as Peter Drucker noted. Not just the big, strategic decisions of your executives but the day to day decisions that drive your business.After all, people react to your organization’s decisions, especially decisions made by front-line systems and staff, as though they are both personal and deliberateYou should make sure they are
Little decisions add up so focus on operational or front-line decision makingThe purpose of information is to decide so put your data and analytics to workYou cannot afford to lock up your logic so externalize it as business rulesNo answer, no matter how good, is static so experiment, challenge, simulate, learnDecision Making is a process to be managed
Why manage decisions independently of process? What’s the advantage? There are several…Faster, easier, independent changes to decision logicCoordination of decisions across channels and productsSimpler processes that are much easier to manage Higher employee productivity and resource utilization Analytic insights for making better decisionsContinuous improvement of decisions and results
Before we talk about decision services, let’s take a step backIn the “old” days applications were monolithicOver time we have recognized the value of decomposing applications and storing the components in a more declarative, reusable form.
All these pieces contribute to ever-more sophisticated decision services that support your business processes.Decision Services externalize and manage the decisions production processes and systems needBusiness rules allow business users to collaborate in the declarative definition of decisionsAnalytics can create better more data-driven business rulesAnd ultimately additional predictive analyticsAdaptive control allows test and learn to become part of a continuous improvement loop
Here’s another example, this time of an insurance company with about 750,000 policies that implemented a risk-based underwriting decision service for use across its systems. In the first year an eight-point reduction in combined ratio – a big deal for an insurance companyThey got this improvement from all the areas I see when clients apply decision managementThey reduced costs by eliminating many manual reviews and by putting underwriters and actuaries in charge of the rules behind the decision – they eliminated or reduced many of their IT costs.They boosted revenue, the second major area, by improving risk management (far more tiers and more fine grained decisioning) and by focusing their staff on the book of business and helping agents improve it rather than on transactional approvalsThe third area does not show up in the specifics but when I talked to them it was clearly the most powerful aspect of the whole thing. They gained true strategic control over their underwriting decisions.
Infinity Insurance is a $1Bn writer of insurance for classic cars, commercial auto and personal auto. The claims department at Infinity signed up with predictive analytics software from SPSS Inc. in 2007 to target process change in a couple of areas – fraud and subrogation. The Fraud Investigation Unit had an old process for flagging potentially fraudulent claims, involving adjustors manually applying some simple rules. This needed to be replaced with a more transparent system to identify risky claims. In Subrogation, the process of finding other companies who might be liable for some of a claim, Infinity had spent millions of dollars for outside consultants to come in and check for subrogation opportunities. This had yielded $12M – $16M in actual recoveries so it was clear that a better process was possible.Subrogation turned out to be the quickest payoff, with analytically-derived rules being deployed in about 9 months. The new subrogation decision had 33% higher returns – it found subrogation opportunities in many more claims than before - and paid off the analytic software investment in just 3 months. Previously Infinity had about 15% of its claims come in to the subrogation department for review and now over 22% come in. As a result recovery from has gone from $1.2m/mo to $2m/mo!While Infinity was adopting analytic decision making the industry entered a deep recession. Without the analytically enhanced decision-making they were adopting this would have had far more effect on their business than it did. They had to layoff 25% (some 300 staff) and trim expenses. With 12,000-13,000 new claims a month they had to get cases out to field staff quicker, improve customer service and reduce people all at once. The automated identification of fast track claims helped them decouple the number of adjustors from the amount of business being written.To achieve this they started “Right tracking” claims using Risk Control Builder to assign claims to fast track adjustors. This was a new approach, created to take advantage of the new analytic decision making using a combination of business rules, predictive models, and information gathered from customer interactions. These fast track adjustors were people in the business units who went from simply reporting losses to handling some claims. This created a group who could process the claims “once and done” – no hand off to field adjustors. This allows them to open, appraise and pay some claims within 10 days or so. The analytically enhanced decision increased the old fast track rate of 2% to 22% in just a year of operation. The 100 fast track adjustors now handle more than 2,500 claims a month without referral to a field adjustor. This represents a huge cost saving for the field, helps decouple business growth from the number of field adjustors and has reduced their loss adjustment expenses from 14% to around 11%. Infinity uses PASW Modeler data mining workbench to create predictive models and Risk Control Builder to deploy them. Starting originally with external consultants, Infinity has been transitioning to having more of its own staff use the tools. The resulting decisions are a mix of rules specification and analytics work and Infinity has been identifying people who have real claims experience and getting them involved in managing the rules themselves as analysts. This enables new rules, new models to be deployed without going through IT and this is important as IT has been a challenge.Initially IT was skeptical about the approach and was not all that supportive – the business had to get past a “this sounds like star wars” mindset. The business needed their support but IT was getting cut back and so appropriate teams were not always available. Over time the business has reduced its demands on IT as claims personnel have become able to add rules. With weekly changes to the rules and models this ability to make their own changes has been critical to delivering the agility and responsiveness they needed.The different decisions are each handled in slightly different ways. In the Fraud Investigation Unit the analytic models and rules are used to create a credit-score like ranking. The decision is to determine the fraud risk score for a particular claim. This score is then used by the team to rank order claims so that those with the highest risk of fraud are investigated first. The decision as to whether or not a claim is suitable for fast tracking is similar, with the score being used to send the most appropriate claims to fast track. The subrogation decision is a little different with the rules and analytics being used to identify those claims where no subrogation has been identified but where it is considered likely. Infinity is building a new claims system with some self-service options and an ability to support mobile devices and automated claims payment. This new system is being developed with hooks to the analytic decision making process to allow for no-handle claims.Infinity found that board members can be hard to convince when it comes to claims as numbers for things like fraud are often imprecise. Subrogation was easier to “sell” in part because the numbers are easy to see – claims handed off to others to pay are very concrete. Successful adoption requires finding the pain points of executives and showing how the technology, the approach, can address those pains.
Because operational decisions are high volume, transactional decisions IT is involved (systems)Business people are involved in decision makingAnd we want to bring analytics to bearThree groups not always that good at working togetherBusiness does not understand code nor does it understand the mathAnalytics people are not familiar with IT nor do all analytics folks think about IT when they are building modelsIT people think of data as something to be stored and reported on, not something to be usedThe business – and what they know and don’t know – represents a whole other class of problemBusiness people understand their business and they know what results they want, want will count as a successDo they understand the math in your models? NoDo they understand the statistics in adaptive control and model performance? NoDo they understand programming code that represents the actions they need taken? NoTo succeed long term you must engage the business in the comparison of ideas, of different approaches, in the results of analysis and in the actions being taken. This means providing them with an environment that lets them collaborate with you without having to become LIKE you.In retention terms this means showing them the effect of different retention strategies on customer segments named the way they name them and in terms they understand – not statistical measures but business measures, the KPIs they use every day. It means letting them specify the retention offers in a simple, declarative format they understand (rules, say).Another challenge is the lack of awareness of analytics and data mining, their power and how to apply them among IT professionals.For instance imagine a conversation:A business user realizes it would make it much easier for our call center to handle customers if we knew this about them. If they ask IT they will prompt an IT-centric response – who knows it and could enter it? Which application(s) do they use? What table structure can we use to store it? How about backup, security, recovery…… If they ask someone with an analytics background they will get a different one – I wonder how we could derive that from what we know about our customers….When systems are developed or modified, a need for attributes, about a customer say, results in a data design along with an application to capture the data and, in the end, a database containing the information. It’s a one way street – from need to data in a database.Analytics can close that loop – taking the historical data being created and deriving new, useful attributes from it.Of course this only works if the IT people know to ask and it’s a lot simpler if IT is cleaning, storing and making available the data that analysts will actually need (not some aggregation of it suitable only for reporting).In our example if the business users tell their IT folks that they need to include the retention risk and long term profit potential of a customer in the retention offer logic the result may be a project to capture this information and store it in the database. It is unlikely to result in an analytic project to derive it.When IT does impact analysis they don’t consider modelsWhen the business does impact analysis they don’t eitherModels are part of a complex ecosystem so impact analysis is a problemIf I change the KPIs for the customer managers that changes their definition of good results and thus of a good model. Would such a change ripple through to the analytics team? If the IT team implements a change to the customer database design that will change the historical data available from that point on – will that ripple through?
Begin!Identify your decisionsHidden decisions, transactional decisions, customer decisionsDecisions buried in complex processesDecisions that are the difference between two processesConsiderWho takes them nowWhat drives changes in themAssess Change ReadinessConsider Organizational changeAdopt decisioning technologyAdopt business rules approach and technologyInvestigate data mining and predictive analyticsThink about adaptive control
Decision Management Solutions can help youFind the right decisions to apply business rules, analyticsImplement a decision management blueprintDefine a strategy for business rule or analytic adoptionYou are welcome to email me directly, james at decision management solutions.com or you can go to decision management solutions.com / learn more. There you’ll find links to contact me, check out the blog and find more resources for learning about Decision Management.
The top 10 imperatives for Insurers and the role of Decision Management in addressing them James Taylor, Decision Management Solutions Deb SmallwoodSMA Strategy Meets Action
CHART A COURSE TO PROFITABLE MARKETS … plot a position for growth with market intelligence TARGET PRICE MANAGE SELL A winning path to profitable growth involves more than just marketing. Increasing profit potential requires employing data, tools, engines, and process automation across multiple capability areas – ensuring that the right market opportunities are chosen, the right risks are selected, the right pricing is determined, and the right management and service is delivered. Growth and profit start with the alignment of product, customer, and channel. EXTERNAL AND INTERNAL DATA ANALYTICAL TOOLS PRECISION ENGINES AND AUTOMATION
Decision Management An approach or business discipline for automating and improving decision-making It improves day to day business results by Supporting Automating and Improving operational decisions It builds on existing enterprise applications to put data to work manage uncertainty increase transparency give the business control
The top 10 imperatives Chart a course to profitable markets Put the data to work Rethink legacy Fast path new product development Drive dynamic distribution channels Apply smarts to underwriting Capitalize on intelligence to manage losses Holistically link customer communication Benefit from business optimization Make governance work
Decision management and underwriting 6. Apply smarts to underwriting
Decision Management and claims Claims Innovation 12,000-13,000 claims/month Recession – lower expenses, 25% fewer staff Claims adjudication with rules, analytics Results 1100% increase in fast track rate Loss adjustment expense from 14% to 11% 33% higher subrogation returns Subrogation recovery up by $10m/year 7. Capitalize on intelligence to manage losses
Operational decisions at the center 10. Make governance work
SMA Top 10 Imperatives – Next Steps There will never be a better time to take a breath, evaluate, and chart the course. Step one is to lay out an IT roadmap. At a minimum it must outline the priorities for the business and then align IT investments to fuel the capabilities for your strategy. Two enablers form the core of the top 10 imperatives: a data game plan and a modern architecture – the foundation for flexibility and responsiveness . Plan, prioritize, and press onward.