WSIB presentation at the Chief Analytics Officer, Fall 2016

Workplace Safety and Insurance Board | Commission de la sécurité professionnelle et de l’assurance contre les accidents du travail

Machine Learning – The Value add to

Our Services

Christina Hoy

VP, Corporate Business Information and Analytics October 5, 2016


WSIB – Who we are

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A “hybrid” – independent trust agency in

Ontario, Canada:

- legislated by the Ontario government and

responsible for administering the

Workplace Safety and Insurance Act

(WSIA)

- funded by the employers of Ontario

Administers compensation and no-fault

insurance for Ontario workplaces

Serves workers and employers ranging from

small businesses to large private- and public-

sector organizations Source: By the Numbers: 2015 WSIB Statistical Report www.wsibstatistics.ca

http://www.wsibstatistics.ca/


Then and Now – Strategic Change at WSIB

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Defining Machine Learning


Machine Learning at WSIB

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Operationalizing Machine Learning can transform WSIB, moving beyond monitoring, to delivering new value added services

Machine learning means strategically investing in advanced analytics tools that add value to WSIB. Machine Learning will enable WSIB to:

– Bring effective innovative solutions to better serve our customers and improve outcomes,

– Improve decision making in real-time by embedding machine learning into business decisions made at the front-lines of our

operations,

– Build a culture of continuous improvement by implementing a feedback loop between decision makers and senior management, and

– Scale up the use of data by pulling together information from a number of disparate sources to create models that better target customer segments and geographies to produce highly specific products/services for our customers.


Machine Learning

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Examples of Machine Learning at WSIB

The following are early examples

of machine learning:

■ Claims Risk Scoring

■ Fraud Detection

– Employer Premium

– Service Provider

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Machine Learning at WSIB implements a continuous feed back loop


Claims Risk Scoring Current:

■ Claims risk scoring answers the question; how do changes to different factors influence the outcome of a claim at a specific time after injury?

■ In order to address this question our data scientists worked collaboratively with senior management to understand the business issue.

■ Once the issue was understood, a review of the available data took place.

■ We then used SAS tools to bring this data together from a number of disparate sources.

■ Finally, a model was created and supervised machine learning techniques were applied.

■ The business reviewed the outputs of this model for acceptance and we then

successfully deployed to middle management for use in prioritizing

the review of claims.

Future:

■ Our plan is to deploy this model to front-line decision makers by

integrating directly into our front-line systems.

■ By doing so we hope to implement a decision

management system for feedback loop to assess

assess the impact of decisions made.

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Claim Risk Scoring Example

Joe is a 45 year old male. He is currently employed in the Forestry industry

and is an equipment operator.

Joe is a French speaking Canadian who has never

had a prior claim.

Joe works for a private company with less than 20

employees and makes approx. $500 per week.

Last week Joe fell at work and was injured.

Back Sprain

Shoulder Leg fracture

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Probability of staying on benefits changes for different injury types


Fraud Detection

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Current: ■ How do we better understand and identify fraud, waste and abuse.

■ To do so we created two fraud detection models to use as a targeting tool for high risk employers and service provides:

Employer Premium Reporting, and

Service Provider.

■ Once we understood the business issue we reviewed both internal and external data and combined them using specialized software.

■ A model was then created and we applied both unsupervised

and supervised machine learning techniques.

■ The outputs of this model were then reviewed by

our front-line business for acceptance, at which

point we deployed in the form of reports

and target lists.

Future:

■ As with our older model we plan to deploy this to

front-line decision makers which will enable a more

robust feedback loop on decisions.


How did we embed this into our operations?

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Formal policies,

systems and practices

Informal practices, and

symbolic actions

Belief, values and

attitudes

The key to success is to create an Analytics Driven Culture


Building the right team

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At least four different skill streams are required:

Design the

necessary infrastructure

Utilizing data

produced by data science

Wrangling data

from data marts and warehouses

Constructs, designs

or arranges usable data


Continuous Improvement: the Machine Learning Journey

■ Continue culture shift among senior management by building trust through small

scale focused initiatives with high potential successes.

■ Embed machine learning by implementing these models

into the tools that front-line decision makers use.

■ Fill technological gaps by strategically investing

in new tools and products that enable the use

of machine learning techniques more

effectively.

■ Continue to build in invest in our Information

Management Architecture.

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WSIB will strive to create an environment of analytics throughout the

organization to spur creativity in how we solve seen and unseen problems


Our Journey Continued…

■ Continual improvement of data quality and access by implementing a data Quality

Assurance Program.

■ Change in team structure to ensure our workforce has

the right capability to meet the needs of a data

driven future.

■ Drive innovation in data analytics by aligning

a strategic team of experts with a common

focus on maximizing business objectives

through a Centre of Excellence.

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WSIB will strive to create an environment of analytics throughout the

organization to spur creativity in how we solve seen and unseen problems


Questions

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WSIB presentation at the Chief Analytics Officer, Fall 2016

Data & Analytics

Transcript of WSIB presentation at the Chief Analytics Officer, Fall 2016