2018 predictive analytics symposium - soa...automatic code export. how datarobot helps data...

2018 Predictive Analytics Symposium

Session 14: DevelSmarter Decisions: How Automated Artificial Intelligence (AI) Is Changing The Insurance

Industry

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How Automated Artificial Intelligence (AI) Is Changing The Insurance Industry

Rajiv ShahData ScientistDataRobot

Confidential. Copyright © DataRobot, Inc. - All Rights Reserved

1. Introduction to Automated Machine Learning

1. Challenges and Opportunities

2. Succes s Stories

Agenda

Confidential | Copyright © DataRobot, Inc. | All Rights Reserved

AI will generate $2.9 TRILLION in business value and recover 6.2 BILLION hours of

worker productivity by 2021.

- Gartner Predictions (Forbes) -

THE IMPENDING AI DIVIDE

Machine Learning

Deep Learning

Powered by rules

Powered by deep learningPowered by machine learning

Decision Making in 21st Century

The AI Bottleneck: Data Scientists

DomainExpertise

Programming Skills

Math & Stats

Prerequisites

Domain ExpertiseKnowledge of the overall & specific missionsKnowledge of the data

Programming SkillsAbility to write code to gather dataAbility to write code to explore/inspect dataAbility to write code to manipulate dataAbility to write code to extract actionable intelAbility to write code to build modelsAbility to write code to implement models

Math & StatsFoundational statisticsInternals of algorithms Practical knowledge and experienceKnowing how to interpret and explain models


HAND-CODING EVERY MODEL CANNOT POSSIBLY MEET THE DEMAND

In order to recognize the potential of AI, organizations need to utilize tools that will accelerate adoption

Time

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100

50

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150

Machine Learning Automation:The New Prerequisites

DomainExpertise

Programming Skills

Math & Stats

Prerequisites

Domain ExpertiseKnowledge of the overall & specific missionsKnowledge of the data

Automated

AI Opportunities in Single DivisionData Scientists

AI Opportunities in Single DivisionData Scientists + Business Analysts

ENABLING THE AI-DRIVEN ENTERPRISE


AUTOMATED MACHINE LEARNING GREATLY INCREASES CAPACITY

Time

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100

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Automated machine learning dramatically increases productivity

Automated Machine Learning

http://www.youtube.com/watch?v=RrbJLm6atwc



2. Succes s Stories

Agenda

Confidential | Copyright © DataRobot, Inc. | All Rights ReservedConfidential | Copyright © DataRobot, Inc. | All Rights Reserved

EVERYONE ELSE IS RACING TO GET THERE FIRST

A horde of insurtech companies are out-innovating large financial institutions

But the established insurers have more expertise and more data. They will win if

they harness the power of AI and ML

DO

CU

MEN

DAT

A PR

EP

SWAM

P

DataManagementData cataloging, organization, and collaboration. Automatic indexing and knowledge gathering made available to the entire organization.

Prep, Blend,Agg and ETLData prep, blending, transformation, feature engineering, and sharing of insights. Data pipeline and workflow execution.

AnalyticsSimple: Self-Service BI, charts, graphs, tables, queries.Advanced: Automated data investigation for insights, predictions, and recommendations

Model Risk ManagementSimple: Self-Service BI, charts, graphs, tables, queries.Advanced: Automated data investigation for insights, predictions, and recommendations

DeploymentPowering business applications by providing advanced analytics insights, predictions, monitoring, and refresh on new data. Hosted as an API, SDK, or code.

ConsumptionConsuming and application of advanced analytics in the form of dashboards, decisions, and analytics powered applications.

REBU

ILBEN

CH

MAR

HOW DATAROBOT HELPS DATA SCIENTISTS AND QUANTS DO MORE FASTER

V A L U E

ConsumptionDeploymentModel Risk ManagementAnalytics

Prep, Blend,Agg and ETL

DataManagement

Automatic model documentation

Automatic validation testing

Automatic model monitoring and refresh

External model validation

Integration with data prep tools like Trifacta

Fast iteration on data prep

Automatic flagging of data issues

Automatic text mining, parsing and processing

Automated data cleaning; e.g., missing values, binning, credibility

Automatic feature engineering and selection

Automatic use of best practices

Automatic benchmarking

Automatic model tuning

Transparent model diagnostics

Horizontal and vertical parallelization

Code free development

Much more...

Drag and drop scoring

Production- grade API with monitoring

Distributed scoring

Automatic code export

HOW DATAROBOT HELPS DATA SCIENTISTS AND QUANTS DO MORE FASTER

Transparent models for easy understanding

Wide range of model classes, including familiar statistical models and decision trees

Prediction explanations to understand model behavior

Integrations with databases and dashboarding tools

Decision Making in 20th Century

Powered by rules, heuristics, and spreadsheets

© DataRobot, Inc. All rights reserved.

Modeling is dangerous .

Leave it to the profes s ionals

At a top 10 US Bank, we were completely s tonewalled by the reta il data s cience team, who preferred to only use PhD level data s cientis ts .

The next month, bus ines s analys ts (non-data s cientis ts ) in another department used DataRobot to uncover a $300M+ use-case




There’s no way that that will meet our

guidelines / regulations

Model governance teams at a large bank were skeptical that modern machine learning models could survive their internal model approval proces s

The firs t five DataRobot were models approved a t the bank in weeks ins tead of the usual months




We don’t do it that way here



Infras tructure and architecture teams ins is ted on us ing legacy methods to deploy models , which including implementing raw scoring code

Model deployments took months until they evaluated a spark-scoring method, which was implemented the next day




We don’t do it that way here



I don’t need AI to do my job. I’m an expert

Frus tra ted with the firs t itera tion of lead s coring models the bus ines s champion walked away from the project, preferring to do things the old way

Subsequent itera tions resulted in $10M+ benefit to the bus ines s from increase convers ion ra tes



2. Succes s Stories

Agenda


Pet Insurance● Lead scoring● Automated invoice payment

/ denial● Renewal retention prediction● Pricing

Risk Management● Fraud detection● Model risk and model

documentation● Dispute development● Anti money laundering● Emerging risks● Risk scoring

Sales and Marketing● Cross-sell / Up-sell● Web page banner and online

ad optimization● Lead scoring● Single customer view● Smart customer dashboards● Lifetime customer value

Underwriting● Automated underwriting

acceptance● Triage problem applications● Prioritise medical tests● Prioritize questions on

application form● Change in quality of business

Finance● Budgeting● Fraud detection● Cashflow projections● Automated expense

authorization● Suspicious transaction

identification

Pricing● Technical pricing● Dynamic pricing● Price elasticity● Competitor prices / market

ranking

Life Insurance● Lead scoring● Underwriting medical

conditions● Lapse prediction● Roboadvice● Cross-sell / up-sell

Actuarial● Technical pricing● Claim reserving● Price elasticity / dynamic

pricing● Risk scoring

Investment Management● Credit risk● Economic forecasts● Market dispersion

General Insurance / P&C / Casualty● Dynamic pricing● Fraud detection● Predict and avoid claims

litigation● Risk scoring● Identify salvage and

subrogation opportunities

Medical Insurance● Automated underwriting

acceptance● Fraud detection● Automated claim invoice

payment / denial● Trailing invoice prediction

THERE ARE HUNDREDS OF OPPORTUNITIES TO OPTIMIZE EVERY LINE OF BUSINESS IN AN INSURER

Claims● Fraud detection● Predict and avoid litigation● Dispute development● Automated invoice payment

/ denial● Identify salvage and

subrogation opportunities

1. Cros s -s ell and Up-s ellIt costs less to sell more to an existing customer than to bring in a new cus tomer. DataRobot lets you individually optimize which mes s ages you us e to connect with cus tomers and which products you s ugges t, driving greater s ales .

2. Web Page Banner and Online Advertis ing OptimizationBus ines s es s pend billions of dollars a year on advertis ing, but is that money well-s pent? With DataRobot, marketers attribute s ales to advertis ing activities , optimizing ad s pend to bring in more leads for les s .

3. Lead s coringIdentifying and engaging high-quality leads is critical to s ucces s , but mos t bus ines s es us e gues s work for pros pecting. Us ing DataRobot to predict what content res onates with each pros pect improves clos e rates us ing data that bus ines s es already have.

4. Single Cus tomer View and Smart Cus tomer Das hboardsFind duplicate cus tomer records to merge into a s ingle cus tomer view to better unders tand your cus tomers . Treat your cus tomers as individuals by building s mart cus tomer das hboards that predict future behaviour e.g. laps e and the product that they are mos t likely to purchas e next.

5. Lifetime Cus tomer ValueIns urance can be a long-term relations hip. Es timate the projected future value of your cus tomers .

Sales and Marketing

What are key models for...

1. Automated Underwriting AcceptanceConsumers are becoming more demanding and will switch to competitors if their ins urance application proces s ing takes too long. With DataRobot, mos t applications can be automatically accepted or rejected within s econds .

2. Triage Problem ApplicationsHow much time do your valuable underwriters s pend rubber s tamping ins urance applications ? DataRobot can triage difficult and complex cas es to s enior underwriters for their expert judgement.

3. Prioritizing Medical Tes tsMedical tes ts are prudent for ins urance applications , but each tes t cos ts money and over many applications this adds up to a lot of money. DataRobot can learn which applications truly need medical tes ts , and which don’t, reducing underwriting expens es without s acrificing quality.

4. Prioritizing Ques tions on Application FormsCons umers can become frus trated filling out application forms . DataRobot can identify which ques tions are important, and which are redundant, s treamlining your application forms .

5. Change in Quality of Mix of Bus ines sAdvers e s election can be a ris k, even when you maintain underwriting s tandards . DataRobot can s ift through new policy data to automatically identify changes in bus ines s mix and predict likely changes in future profitability.

UnderwritingWhat are key models for...

1. Technical PricingClaims are typically the largest cost in insurance, but they can also be difficult and time cons uming to predict. DataRobot builds validated rating tables that can be downloaded for us e in pricing.

2. Claim Res ervingTraditional actuarial techniques s ummaris e claims into development triangles , upon which res erving techniques s uch as chain ladder can be applied. But s ummaris ed data provides few data points for es timating inflation, and can hide s tructural changes in an ins urance portfolio that could be mis interpreted as inflation. Thes e is s ues can be overcome by building s tatis tical cas e es timate models that predict the ultimate cos t of individual claims , a llowing for their individual characteris tics including text mining of claim des criptions .

3. Price Elas ticity / Dynamic PricingDis cover the effects of pricing on cus tomers ’ decis ions to renew or purchas e. Optimize your pricing by adapting to changing market conditions , a llowing for both s upply and demand.

4. Ris k ScoringUs e his torical data to quantify and rank the quality of ins urance ris ks , es timating frequency and s everity.

ActuarialWhat are key models for...

1. Fraud DetectionFor claims fraud detection, AI far exceeds the effectiveness of legacy rule -bas ed methods , with fewer fals e pos itives and fewer mis s ed opportunities for claims s avings .

2. Predict and Avoid LitigationPredict which claims are likely to res ult in litigation, and minimize unneces s ary legal cos ts by pro-actively making targeted s ettlement offers .

3. Dis pute DevelopmentTriage dis putes that are likely to develop into problem claims .

4. Automated Invoice Payment and DenialStreamline the proces s of s ubmitting and res ponding to claims by triaging complex decis ions to s enior claims s taff, while automating invoice payment and denial for mains tream claims .

5. Salvage and SubrogationReduce claims cos ts by identifying opportunities for s alvage and s ubrogation.

ClaimsWhat are key models for...


Underwriting Model: Identifying the 10% of customers with 5x mortality rate

Model built in 4 hours had a 85% accuracy versus a 64% accuracy model that took 2 weeks to build. Saved over $60 million annually.

ACCURACY


Subrogation Model:

With one press of a button they had a 20% lower error rate compared to a hand built statistical model.

ACCURACY


Propensity to Buy:

It took 30-40 minutes to get a list of two dozen proposed models. This would have easily taken several months of a statistical resource.

TIME TO BUILD


Insurance Pricing:

Able to deploy models in days versus months by moving to an API based deployment strategy.

OPERATIONALIZE: TIME TO DEPLOY

Operationalize

“The combined power of DataRobot and AWS have transformed the ability of our small team of 3 to build and deploy models in a fraction of the time, allowing us to deliver customer based pricing when underwriting insurance policies”

- Paul XYZ, Data Science Manager, XYZConfidential. Copyright © DataRobot, Inc. - All Rights Reserved

Highly accurate models, faster

Real-time pricing platform1

A Floor of Pricing Teams:Actuarial – Optimisation –Innovation etc

2 3-4 Weeks Model Building

3 Understanding / Analysis

4 Slow Implementation If Possible

Traditional

✓ 3 Data Scientists

✓ 1 Week (Or Less)

✓ Model Metrics Out Of The Box

✓ Simple API Call

XYZ

Better Overall Results Than

Traditional Methods

Digital Transformation:❖ from intuition-based pricing in

Excel ❖ to Data-driven price optimisation

in 1 year with 3 analysts.


DESCRIPTION OF OPPORTUNITY

VALUE/ROI CALCULATION NUMBER OR EVENT TO PREDICT

EASY

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CLIENT DESCRIPTION

Rule based fraud detection is not as accurate a t identifying problem cla ims as modern machine learning models

Whether a cla im is rejected as fraudulent for auto ins urance cla ims

Overnight batch run predicting the probability of fraud on newly reported cla ims . Thos e cla ims s coring high probabilities are triaged to a s pecia lis t cla ims fraud team for inves tigation.

> 10,000,000

INSURANCE

***- Avoid 50% of fraudulent cla ims- Increas e fraud detection

accuracy by 30%




EASY

LOW

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CLIENT DESCRIPTION

Manual cla ims proces s ing is not as accurate a t identifying problem cla ims as modern machine learning models

Whether a cla im res ults in litigation for workers compens ation ins urance cla ims

Overnight batch run predicting the probability of litigation on newly reported cla ims . Thos e cla ims s coring high probabilities are triaged to s enior cla ims s taff for an earlier and more a ttractive s ettlement offer.

> 5,000,000

INSURANCE

***- Avoid 10% of litigations- Decreas e cla ims cos ts on a t-

ris k cla ims by 25%




EASY

LOW

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CLIENT DESCRIPTION

Standard GLM-s tyle pricing models are not as accurate as modern machine learning models

Expected los s es (pure premium) for a policy year for auto ins urance policies

Integrate the machine learning model (XGBoos t) with the pricing tool in order to provide the right price in real-time

3,000,000

INSURANCE

***- Decreas e LR by 5%- Improve cus tomer retention 5%- Improve acquis ition cos t by

10% over 5 years




EASY

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CLIENT DESCRIPTION

Some cla ims that have the opportunity for subrogation may not be identified (or identified fas t enough) to benefit

- Expected recoveries due to s ubrogation: €2M

- Increas ed s ubro ra te from 1.4% of cla ims to ra te to 1.6%

- 5% increas e in s ubrogation (due to better targeting)

Which cla ims have a high likelihood of recoveries through s ubrogation?

Weekly batch proces s ing to analyze open cla ims , identifying thos e with the highes t likelihood of s ubrogation being pres ent. Pres ent lis t of likely cla ims to cla im handlers via tableau das hboard

450,000 per year

INSURANCE

***




EASY

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CLIENT DESCRIPTION

Credit reports may not a lways be neces sary for a ll cus tomers for underwriting insurance policies

- Credit reports cos t €1- 500,000 credit reports reques t

per year- Los s es due to fa iling to order

the credit report when needed: €200, bas ed on s urcharge for thos e with fa iling credit s core and combined ra tio

70,000 per year

Will the cus tomer have a fa iling credit s core?

INSURANCE

Score new applicants in real-time to determine whether or not they are likely to have fa iling credit. If their prediction is greater than the thres hold, reques t the report, otherwis e do not.

***




EASY

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CLIENT DESCRIPTION

- Reduce cancella tions by 1%- Improve combined ra tio (via

more accurate pricing) by 0.7%- Variable cos ts : 24%

Non-renewal cos ts insurers money, and the mos t profitable cus tomers are mos t likely to churn

350,000 per year

Will a policy non-renew?

INSURANCE

When determining the renewal price change (RPC), in real-time calcula te the ris k of churn. Us e this as an input, a long with expected combined ra tio to determine the final RPC.

***


Automating Predictive AnalyticsSession 31

3:40


WHAT TO WATCH FOR IN THE WORKSHOP

Best practices and guardrails automatically

applied

Automation with flexibility for experts

Full transparency. No black box models

Automatic benchmarking/challenger models

Automated Model Documentation

Flexible deployments and automatic monitoring

2018 predictive analytics symposium - soa...automatic code export. how datarobot helps data...

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