The Product Development Section Presents Underwriting Issues & Innovation Seminar · The Product Development Section Presents Underwriting Issues & Innovation Seminar July 31-August

The Product Development Section Presents

Underwriting Issues & Innovation Seminar July 31-August 1, 2017 | The Westin O’Hare | Chicago, IL

Risk Assessment Tools – Part 1

Moderator:

Cindy Mitchell

Presenters: Robert Stout

Brian Lanzrath Dianne Schuetz

Eric J. Carlson, FSA, MAAA

Underwriting Issues and InnovationEric CarlsonJuly 2017

Agenda

What is Risk Score

Simplified Issue Case Study

Implementation Options

Rx and RxRules Background

Fully Underwritten Case Study

The Future of Underwriting

Electronic requirements (Rx, MIB, MVR, Medical, Credit …)

Decision engines driven by data

Predictive Models

Automation

Increasing

APS, Labs

Cycle times

Costs

Decreasing

Better Customer Experience

Rx and mortality

4

2015Milliman mortality study 53M exposure years 13M lives 231,000 deaths Created Milliman Risk Score

Health Plan

PBM

Clearing House

RetailPharmacy

Access (with authorization) to Rx Histories on more than

200 million Americans.

Milliman has accumulated a large Rx and mortality data set.

Rx Histories

1 PrescriptionBrand and generic name | Dosage and quantity | Date of fill

2 PhysicianSpecialty | Contact information

3 PharmacyContact information

4 Dates of eligibilityWith or without prescriptions

5 Underwriting significance indicatorRed, yellow, green

RxRules interprets prescriptions and other inputs.

RxRulesData Input Rx data Application data MIB / MVR Medical data

UW Guidance Conditions Severity Decisions

Rule Variables Indication / Therapeutic class Drug combinations Fill timing(date or duration ranges) Fill counts / patterns Dosage / quantity Physician specialty / count Gender / Age Other variables

RxRules – Dosage Matters

Trazodone147% relative mortality

Low Dose

132%

High Dose

224%

RxRules – Drug Combinations Matter

Spironolactone209% relative mortality

Thiazide Diuretics (102%)

Ace / Angio II (ARBS) (116%)

Beta Blocker (122%)

With 2 out of 3 of:

328%

Thiazide Diuretics (102%)

Ace / Angio II (ARBS) (116%)

Beta Blocker (122%)

Without 2 out of 3 of:

166%

Milliman Risk Score

RxRules-driven predictive model

Predicts relative mortality of a life or group of lives

Holistic, multi-variate Rx model of mortality risk

Delivered within RxRules

The Milliman Risk Score is built on RxRules.

Milliman Risk Score

250,000NDC codes

7,500GPI codes

Hundreds of RxRules

1.27

Milliman’s Risk Score effectively predicts mortality.

0%

100%

200%

300%

400%

500%

600%

0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 40

400,000

800,000

1,200,000

1,600,000

2,000,000

2,400,000

Milliman Risk Score

Rel

ativ

e M

orta

lity

Indi

vidu

als

Relative Mortality and Lives by Milliman Risk Score

Lives Relative Mortality

Source: 2015 Milliman Mortality Study: 13M lives, 8M Rx hits, 230K deaths

Risk Score Applications

1 Individual Underwriting

2 Group Underwriting

3 Inforce Analysis

4 Market Segmentation

5 Pension Risk Transfer

SI Case Study Background

Auto-decision via RxRules

Risk Score as of time of underwriting

Have deaths on issued and declined cases

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 > 4.0

# of

Liv

es

Risk Score

Risk Score Distribution by UW DecisionSI Case Study #1 - Hits Only

Lives - Issue Lives - Decline

SI Case Study – Distribution of Lives

Issue

Decline

Average Score (Hits Only)

Issue 0.96

Decline 1.52

0%

100%

200%

300%

400%

500%

600%

0.00 ≤ x < 1.00 1.00 ≤ x < 1.50 1.50 ≤ x < 2.00 2.00 ≤ x < 3.00 3.00 ≤ x

Rela

tive

Mor

talit

y

Risk Score Range

Relative Mortality by Risk Score and UW DecisionSI Case Study #1 - (Hits Only)

Issue Decline

SI Case Study – Relative Mortality

Decline

Issue

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 > 4.0

# of

Liv

es

Risk Score


Thresholds can be adjusted to achieve desired business results.

Low ThresholdHigh Threshold

02,0004,0006,0008,000

10,00012,00014,00016,00018,000

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 > 4.0

# of

App

s


Some issued premium now gets declined

Equal amount of declined premium now gets issued

Set Risk Score threshold to issue the same amount of business.

02,0004,0006,0008,000

10,00012,00014,00016,00018,000

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 > 4.0

# of

App

s

New Issues New Declines

02,0004,0006,0008,000

10,00012,00014,00016,00018,000

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 > 4.0

# of

App

s


Threshold

83%

Before Risk Score

75%

After Risk Score

Issued Cases Relative A/ESame amount of business issued

$4 Million increase in profit

9% Mortality improvement

Set Risk Score threshold to maintain the same mortality A/E.

Some issued premium now gets declined

More declined premium now gets issued

02,0004,0006,0008,000

10,00012,00014,00016,00018,000

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 > 4.0

# of

App

s


02,0004,0006,0008,000

10,00012,00014,00016,00018,000

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 > 4.0

# of

App

s


02,0004,0006,0008,000

10,00012,00014,00016,00018,000

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 > 4.0

# of

App

s


Threshold

$56.1 M

Before Risk Score

$66.0 M

After Risk Score

Premium IssuedSame mortality A/E

$2.9 Million increase in profit

18% More issued business

Fully Underwritten Case Study

More difficult to have sufficient claims

Generally distribution of score and highlighted cases only

Fully Underwritten Opportunities to Improve Risk Selection

Issued CasesStatus = Active or Death

# Lives # Deaths Total Face Claimed Face

Score > 2.0 1265 18 $1.2 B $37 M

Score > 3.0 204 7 $217 M $11 M

Risk Score adds value to fully underwritten policies.

Issued

Declined

Issued policies that should have been declined

Implementation considerations and options

Fully automated – SI and Final Expense

Accelerated Underwriting

Fully Underwritten

Reinsurance support

Thank youEric Carlson, Life [email protected] 262-641-3537

mailto:[email protected]

examone.com

Risk Identifier

Medical History-Driven Mortality Risk Assessment

SOA Underwriting Issues and Innovations

July 31st, 2017

???

???

Potential Causal(Indirect Relationship) Mortality Risk

Correlation & Causation in Predictive Modeling for Mortality

ClinicalHistory

PrescriptionHistory

Medical Risk

Self-Disclosure

Tobacco Risk

Behavioral Risk

MVR’s

Criminal History

InsuranceTesting

Vocational Risk

Occupation

Socioeconomic Status

CreditHistory

ZipCode

Education Income

Personality Traits(conscientiousness, risk aversion)

???

Lifestyle

SocialNetworks

PurchaseHistories

Subscriptions

???

???

Known Causal(Direct Relationship)

3 CONFIDENTIAL – For internal circulation only

ScriptCheckPrescription Histories and Mortality


Leveraging the most comprehensive prescription history dataExclusive access to Pharmacy Benefit Manager data

ExamOne works with the largest Pharmacy Benefit Managers in the country

Exclusive relationship with the 2nd largest PBM in the country, OptumRx

Includes data from Catamaran acquisition Expected lift in hit rates of 5-10 points

40% of all maintenance drugs are ordered directly from PBMs

Continues to be one of the most reliable tools to protect from applicant non-disclosure

PBM


Selected ScriptCheck univariate risk profiles

Drug Class Male Mean Age Cotinine+ Hazard Score Risk IQ 99

Insulins 57.3% 53.2 8.0% 328.7 9.9%

Antihypertensives 60.0% 58.9 5.2% 325.8 8.8%

Smoking deterrent 62.8% 45.8 51.6% 134.4 1.1%

Acne agents 47.5% 32.6 4.2% 111.3 0.5%

Topical hypertrichotics, eyelashes* 2.0% 49.2 5.2% 88.8 0%

Demographics and Risk IQ Scoring

*N=101


Abnormal flags

Ordering Physician

160M+ Unique lives

Test results and

reference ranges

45% Repeat encounters

>400M encounters

50% of hospitals

QuestCheck™

Quick access to physician ordered laboratory testing results

Verifies self-reported medical disclosure

Reduce costs

70% of decisions based off lab results

Leveraging Quest Diagnostics extensive clinical laboratory database


Ordering Account Specialty Applicants % HitsGENERAL/FAMILY PRACTICE 1140 43.1%INTERNAL MEDICINE 633 23.9%OBGYN 617 23.3%OTHER HOSPITAL 138 5.2%UROLOGIST 101 3.8%MULTI-SPECIALTY GRP PRACT 90 3.4%ENDOCRINOLOGIST 77 2.9%CARDIOLOGIST 66 2.5%GASTROENTEROLOGY 66 2.5%RHEUMATOLOGIST 44 1.7%SURGEON 40 1.5%NEUROLOGIST 32 1.2%ONCOLOGIST 24 0.9%HEMATOLOGIST 13 0.5%GERIATRIC 11 0.4%NEPHROLOGIST 11 0.4%PRISON/JAIL 1 0.04%

Selected account specialties

Cardiologist diagnoses adrenal tumorApplicant B: 32-year-old male Account name: BMG Stern CardiologyAccount specialty: CardiologyPanel(s) ordered: Catecholamines,Vanillymandelicacid, metanephrinesResults of note: Elevated adrenal hormones suggest pheochromocytomaICD Codes:

• 786.50 – Chest pain, unspecified• 401.9 – Essential hypertension• 250.0 – Diabetes Mellitus w/o complications• 785.1 – Palpitations• 786.05 – Shortness of breath


Risk Identifier


Risk Identifier scoring: Prescription history risk evaluation scoring

268

589

139

116

Individual drugs scored

HIC3 therapeutic classes scored

Drug-to-drug interactions scored

Class-to-class interactions scored

1,112Total variable inputs


Model Definition Hit rate

Complete Blood Count (CBC)

Unique and only available to Quest Diagnostics 19 component tests:

White blood cell count Hemoglobin Other cell type counts (neutrophils, monocytes, etc.) Red cell distribution width

38%

Comprehensive metabolic panel (CMP)

18 component tests: eGFR BUN Protein panel (ALB, PROT, GLOB) Glucose LFTs (AST, ALT, ALP)

33%

Lipid

6 component tests: Total cholesterol HDL cholesterol Triglycerides CHOL/HDL ratio LDL cholesterol Non-HDL cholesterol

29%

Risk Identifier scoring: 5 interacting component models using QuestCheck


Model Definition Hit rate

Order

Applicant Quest Check history evaluated for presence or absence of 305 common clinical panels.

Most common panels: CBC CMP Lipid panel (Cholesterol, HDL, Triglycerides, etc.) Thyroid stimulating hormone (TSH) Hemoglobin A1c

81%

Physician specialty

Applicant Quest Check history evaluated for presence or absence of 26 common ordering physician specialties

Most common specialties: General/Family Practice OBGYN Internal Medicine Other Hospital

81%

Risk Identifier scoring: 5 interacting component models using QuestCheck


Experience

75 Expertise

Structure of a Risk Identifier scoreCombining analytics insights with expert knowledge

Quantitative score Color score

Derived from a statistical analysis of historical mortality

Equal to the % increase in mortality risk (debit/credit)

May be unavailable for some rare drugs or tests (risk unquantifiable)

Categorization of risk based on expert knowledge and clinical literature

Qualitative (Green-Yellow-Red)

Will be available even for rare results


Predictive value by available data sources

204

336

633

761

1120

0

200

400

600

800

1000

1200

QuestCheck Ordersand Specialties

ScriptCheck Only QuestCheck Orders,Specialties, and CMP

ScriptCheck, allQuestCheck Panels

Risk IQ (full labpanel)

Haz

ard

Scor

e (M

edia

n=10

0)

Data Sources

Relative Risk of 99th Model Percentile


Thank You!

Dynamic Risk SelectorSM

Dianne Schuetz

July 31, 2017

VP, Business Initiatives, U.S. Markets

Accelerated, nimble underwriting

2

A Customer’s Perspective

3

What Accelerated Underwriting Enables You To Do

Issue near-fully underwritten retail

rates

Identify applicants who qualify for fluid-

less underwriting

4

Acceleration Solution Overview

Full ApplicationTele-Interview & Age/ Amount Requirements

Does applicant meet the requirements?

Does applicant meet the requirements?

Apply Full Underwriting with Fluids

Accelerated offer

No Fluids Preferred

No Fluids Super Preferred

5

Acceleration Solutions Can Vary

Different designs, approaches, and definitions in the market

Most target elimination of some underwriting elements in the age/amount grid

Can result in a wide range of mortality outcomes

6

Accelerate and balance

mortality and throughput rates

Use keys that fit to meet the

challenge Find the right balance

Acceleration Challenge

7

Dynamic Risk Selector Addresses These Challenges

Applicants want …

• Affordability

• Better process

Carriers want …

Reliable underwriting evidence

New forms of evidence and advanced data analysis techniques are starting to balance the needs of both groups

8

Dynamic Risk Selector

Path to new data and models

Automated acceleration solution built by RGA mortality, underwriting, and data science experts

Streamlined access to expert acceleration rules, evidence, and predictive models

9

Dynamic Risk Selector Components

Predictive Model

TransUnion TrueRisk®

Life

Accelerated Underwriting

Rules

Underwriting acceleration rules Includes application data, disclosures, MIB,

MVR, Rx Targeted rules support the model

Predictive Model Identifies preferred classes for applicants eligible for

acceleration Custom calibration to control mortality slippage

TrueRisk® Life leveraged in addition to

other underwriting evidence to enhance

protective value

10

Process Flow

Dynamic Risk Selector

(Accelerated Rules, Predictive Model)

Client System or RGA Portal

TransUnion TrueRisk®

Life

MIB

MVR

Rx

Future Data Sources

Case Data

Evidence Result/Accelerated Decisions

11

Decision Output

12

Risk Selection Methodology Is Continuously Validated

Parallelunderwriting

Investigatedifferences

Quantifyeffect

Refine andrevise

13

Thank You

The Product Development Section Presents Underwriting Issues & Innovation Seminar · The Product Development Section Presents Underwriting Issues & Innovation Seminar July 31-August

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