The Future of Claims: Optimizing Outcomes

IASAAt the recent IASA Educational Conference in San Diego, FINEOS organized a session entitled “The Future of Claims: Optimizing Outcomes.”  The main part of the session was a great real-word case study provided by Deb Hammond of the Transport Accident Commission (TAC) of Victoria, Australia.

She was joined by FINEOS CTO, Jonathan Boylan and SAS Global Insurance Marketing Director, Stuart Rose.

The session content broadly focused on the roles of analytics and technology in optimizing claims outcomes. Jonathan led off the session and discussed the key elements of actionable analytics and went on to show how the FINEOS Claims system is set up to identify and manage the information gained from analytics

Jonathan went on to say that rules-based claims systems enable rules to be created and implemented within hours and with all claims and integrated data available, rules can instantly result in decisions or scores, allowing for automation or decision support. He warned that automation “is not all or nothing,” and that human intervention and referrals to experts will often be needed, but that automation will help to more quickly and efficiently route issues to the proper expert, maximizing the use of the expert’s time, and speeding time to resolution.

Stuart Rose of SAS then came in to discuss predictive claims opportunities across the entire claims value chain, from notification through to negotiation/settlement.   Stuart ended his segment of the presentation by citing some very impressive results that insurers had seen by taking advantage of some of these opportunities.

Deb Hammond of TAC, who utilizes both FINEOS and SAS then provided her real-world study.  As background, the TAC is a government scheme that provides financial coverage to anyone injured in an automobile accident in Victoria. The Transport Accident Act of 1986 states its purpose as being “to provide, in the most socially and economically appropriate manner, suitable and just compensation in respect of persons injured or who died as a result of transport accidents.”

The primary goal for the TAC was scheme viability, as they are only one of a few schemes like this in the world that have remained viable. The next goal then was a positive client (injured person) experience. And their latest goal is improved client outcomes – getting clients back to work/health as soon as possible. The claims management team has two focused teams – one which focuses on client recovery and the other which focuses on maximizing the independence of those clients who are severely injured and will not recover.

The TAC manages claims for low, moderate and severely injured clients, which involves processing 16,000 claims each year; payments over $1 billion (AUS) in lump sum, income benefits and hospital medical; and benefits for over 40,000 clients each year.

They went through a large overhaul of their legacy systems, bringing 20 individual claims-related systems down to 2 in <year> – one claims management system (FINEOS Claims) and one payment system (still legacy, green screen). The system supports:

  • Claims and Party administration
  • Assessment
    • Eligibility
    • Impairment
    • Income
  • Claims document management
  • Workflow
  • Appointments
  • Payments
    • Lump sum compensation
    • Scheduled benefits

Agility is crucial for the TAC, as clients want their issues resolved quickly, easily and to be kept informed throughout the process. FINEOS supports the TAC to better provide service to their clients:

  • Issues can be recorded by one staff member and accessed by multiple people for resolution
  • Claims history is available to quickly respond to telephone enquiries
  • Work priorities are visible to enable staff to respond to the most important issue for the client

The TAC is continuously innovating and implementing new strategies for business change in order to meet their goals of scheme viability, positive client experience, and improved client outcomes. Deb presented some examples of these innovations with:

  • Auto-allocation of claims to the right team (algorithm)
  • Coordination of client goals (Independence Plan)
  • Facilitation of clients return to work (RAP)

While Deb spoke a bit about each of these initiatives, auto-allocation of claims to the right team speaks directly to the topic of using analytics and technology to optimize claims outcomes, both for the TAC and for their clients.

The TAC has 180 staff members that handle 16,000 new claims per year, managing 24,000 open claims at any given time. The metrics they had for their goals were as follows

  • Improve client outcomes – Bring outcomes for compensable patients in line with those of non-compensable patients
  • Positive client experience – Improve Client Satisfaction score from 7.6 to 8.5
  • Scheme viability – Achieve Actuarial Release of $250 million

Beginning with a simple white boarding exercise, the TAC came up with a risk-based algorithm that would take into account injury type, accident role, pre-existing conditions, employment status, and common law potential to automatically route claims coming in through the call center to the most appropriate claims handler – allowing the more seasoned claims handlers to work on the higher risk, complex, low-volume claims which required more manual intervention, and the newer claims handler to handle the high-volume, low risk claims. The model they came up with was developed using using regression analysis on five years of date (70,000 claims). They put in place monitoring and business rules to catch claims assigned to inappropriate teams (over 80% being automatically accurate), and allowed for adjustable thresholds to help maintain portfolio sizes.


By implementing this algorithm, the TAC reduced the # of claims movements per month by over half, from roughly 1,000 to 400.

They achieved more appropriate portfolio sizes for their claims handlers, re-focusing them on client support as their primary activity.

Lastly, they assigned claims more quickly for earlier intervention. Using the old model, fewer than 30% of claims were going into active management within one month of report, whereas with the new model almost 65% of claims were in active management within a month.

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