Underwriting Transformation with Digital Landscape

While there is digital disruption across the ecosystem can underwriting, the core risk assessment function of the insurance enterprise be insulated. Various factors are impacting the underwriting function viz. changes in consumer needs, technology adoption, consumer buying behaviour, business and distribution models, and regulations. These are enabling insurers to relook at underwriting holistically.

Segments of underwriting portfolio can be further dissected to explore opportunities opened up due to new business models, automation, straight-through processing (STP), data acquisitions, data model-based pricing, sub-segmentation etc. With the emergence of new data acquisition techniques with IOT,  information gateways (medical/ health/ claim/ ratings records bureau), consolidated citizens data etc can aid in automating various manual tasks with various forms/ documents. This information ingestion has been a critical bottleneck for automating rules-based underwriting, risk segmentation, intelligent workflows, 360 degree case management and last but not the least, ongoing learning and rules kaizening.

With the surge in adoption of digital transactions by consumers and also opening of the regulatory restrictions post-covid,  underwriters will be able to expedite automated rules based decision-making based on insightful risk scores, quicker turnaround with field underwriting,  data intelligence based refinements using analytical propensity, claims, lapsation models etc.

Critical capabilities for Efficient & Effective Underwriting Case Management

Case management and better pricing capabilities go hand in hand, and can deliver profitability only when leveraged with an optimal blend of expert underwriting and data based analytics. Various empirical analysis on expert rules based underwriting have universally sought few key capabilities as critically needed for effective and efficient underwriting case management.

# Key Capabilities Why is it Critical?
Rule Configurability
  • Rules will evolve as business learns thus ability to continuously improve and adapt rules is very critical
  • Decisions post proposal submission is no longer acceptable thus field sales demands empowerment.
Reflexive Questionnaire
  • Minimizing back and forth, it is important to intelligently capture critical information/ documents in a reflexive manner at one go, and automated evalaution
Workflow enabled
  • Intelligent orchestration of any underwriting case needs an equally efficient workflow
  • Eligibility rules-based case assignment
  • Rules based auto routing of cases to designated work queues
  • Segmentation of cases based on various parameters such as User Profile, Agent Profile, Sensitive information
  • Ability to manually override case assignment.
Workbench for tracking manual activities
Case Management needs a comprehensive workbench to track all manual interventions with auditing capabilities. Thus, underwriter needs a workbench for,

  • Tool for taking and tracking final decision
  • Raise additional requirements and following up with chasers for medical/ high risk cases.
  • Issue counteroffers
  • Record findings and notes
  • Refer to Re-Insurance
Reporting and Analytical Dashboards
Reporting is needed

  • to analyse further improvements
  • Understanding rule evaluation patterns,
  • Real time tracking
Seamless Integrations
Underwriting needs to be ready for seamless acquisition of data from any third party data sources, viz.

  • Policy admin system for Final UW Decision and further processing
  • Document Management systems for storage and retrieval
  • Master Data Management (MDM) for customer information
  • Third party evidence vendors for credit rating, blacklist checking or
  • other medical / health information as available in respective environments.
  • healthy lifestyle, who allow sharing of critical health data with use of wearables / fitness app

How to increase STP cases

Continuous analysis of decisions and pattern of same will provide cues for which decisions can be further automated. This iterative process to add further information (questionnaire/ document) and effect more improved decision matrix (refined rule).

Thus, kaizening of rules is a continuously iterative process to improve STP %.

# Key Area Why is it Critical?
Information source reliability
Reducing NIGO (Not In Good Order) cases plays important role in obtaining reliable information. As the validation and verification of information at source improves accuracy of data sourcing, validation and verification become further easier and more improved rules can be setup. As more validation rules are refined, automations increases and further manual intervention is reduced.
Decision at last mile
As the rules are moved closer to customer engagement, downstream decisions making process streamlines decision making and with reliable information improves confidence in system  for higher STP rates.
Reviewing the benefit vs residual risk
Need to analyse patterns of past decisions and review the final experience of the same.   STP rules that are automated can be reviewed by business for sometime.  After a thorough analysis of the residual risk of exceptional cases vs the benefits accrued the same can be decided for automated release. Thus such automated decisions emerge from filtering such exceptions and refining the rules. This is a systematic analysis, gradual testing and phased release process.
Effectively kaizening boundary line cases
It is observed that boundary line will continuously change and shift as there will be new patterns that will emerge from

  1. Learning from the reference rule provided by reinsurer data/ UW matrix/ ruled provided from country mortality/ morbidity data.
  2. Learnings of earlier decisions from guardians’ top experts and making them the company standard.
  3. Analytics from past claims experience/ lapsation propensity etc or any additional propensity model that may emerge from statistical big data modelling of actual. This learning can then be translated into effective process of incorporating continuous improvement. But baseline condition clearly becomes capture and availability of data which can be converted to useful information.

Managing UW transformations

While same may look basic, the correct implementation of the said practices determines the success of the case management in any Life insurance. With continuous insights and quest for improvements, boundary line shifts.  Insights emerge with new patterns if monitoring and managing the boundary line cases is effective and very critical for kaizening of rules.

Readiness for collaborating with Insurtech with information gateways (medical/ health/ claim/ ratings records bureau), and abilities to create insightful proposition for niche target prospect data to the extent of segment of one will be critical.

Since 2008 connected devices have outnumbered people and one Mckinsey study anticipates the same to cross 50 Billion connected devices by 2025. This emergence of new data acquisition techniques using IOT, consolidated citizens, health records, claims records data etc can continuously aid in automating more manual underwriting tasks replacing conventional form and document-oriented processes.

Emergence of New Age Business Models and impact on Underwriting

  1. Disruption is being noted in unconventional offerings where pricing is refined to such a narrow segments based on data analytics so as to offer bundled and pre-underwritten policies to narrow customer segments or to segment of one. The pricing offers can be so created based on the customer data insights and turning the conventional process with refined offer for a niche target segment and is more easier with the digital operating models.
  2. Another alternative model evolving is on the usage-based insurance (UBI) esp. in
    + auto insurance where pricing models get further refined for the segment and variables are refined to the usage level as you drive / ride. This has been better enabled with use of telematic devices in vehicles.
    + Rewarding the customers with healthy lifestyle, who allow sharing of critical health data with use of wearables / fitness app.