tackling the growing problem of insurance fraud. · the alarming growth of fraud is making...

Tackling the Growing Problem of Insurance Fraud.

Trends, Technologies, and The Truth About What Will Work.

Tackling the Growing Problem of Insurance Fraud.Trends, Technologies, and The Truth About What Will Work.

© 2019 Daisy Intelligence Corporation.

3© 2019 Daisy Intelligence Corporation.

4 INTRODUCTION

7 FOUR TRENDS AND TAKEAWAYS ABOUT INSURANCE FRAUD TODAY

11WHY REINFORCEMENT LEARNING IS A BETTER APPROACH TO FRAUD DETECTION THAN PREDICTIVE ANALYTICS

12CONCLUSION

13 KEYS TO SUCCESS

15HOW DAISY TACKLES INSURANCE FRAUD

16 HOW DAISY WORKS


INTRODUCTION

The insurance company dubbed him “The Polite

Arsonist” but there’s no point in mincing words

about what really happened.

A 22-year-old man was seen deliberately lighting a

fire in his own vehicle. When someone stopped to

ask if the young man was okay, he responded, “Yes,

thanks. And have a nice day,” before running into

the bush.

This incident happened a few days before his

mother reported the car had been stolen – after her

son told her that his car keys had been stolen at a

house party.

This was one of the top five insurance fraud cases

for 2018 in a report published by Manitoba Public

Insurance but it could have happened anywhere.1

It’s an example of “hard” insurance fraud. While it

makes for a dramatic and even humorous story, it is

just the tip of the iceberg in terms of the growing

fraud challenges facing insurers today.

While it takes imagination and audaciousness to

make claims for services that haven’t been rendered

or to fake an injury, the common tactics in “soft”

insurance fraud – inflating the value of a claim, for

example – might be far more difficult to discover.

According to the Coalition Against Insurance Fraud

(CAIF), fraud costs insurers more than US$80 billion

a year and accounts for 5% to 10% of claims costs

for U.S. and Canadian insurers.2 In fact, nearly

one-third of insurers say fraud is as high as 20%

of claims costs. A recent CAIF survey found that

75% of insurers believe that fraud has “significantly

or slightly” increased since 2014, which was an

11-point increase.3

While it may be difficult to accurately predict the

long-term outlook for insurance fraud, research

suggests the potential to commit such crimes spans

all demographic groups.

A study by the consulting firm Accenture, for

example, discovered that respondents aged 18 to

24 were more accepting than any other age group

of overestimating the value of an insurance claim or

submitting claims for items not lost or stolen.

The survey was conducted seven years ago, which

means that many of the participants are now

prime candidates for insurance coverage. Seniors,

meanwhile, may be motivated to commit insurance

fraud due to financial pressures, health conditions or

other issues.


The alarming growth of fraud is making insurance

companies less profitable and often leads to higher

insurance premiums. Higher prices could also cause

an insurance company to lose business because

customers or prospects do not perceive it as

competitive.

There is a limit, though, to how much insurance

companies can raise their prices. Consumers

and companies are beginning to balk at higher

premiums. As a result, insurance companies are

finding it more difficult to pass on higher costs to

customers.

Insurance companies must be more emphatic in

battling fraud and exploring new approaches and

technology. Not all technologies, however, will

deliver the results insurers need.

This eBook looks at how insurance fraud is evolving,

the best ways to align your teams, and the tools

used to combat fraud.


FOUR TRENDS AND TAKEAWAYS ABOUT INSURANCE FRAUD TODAY

Technology streamlines and automates traditional processes.1

By automating processes, insurance companies

can reduce false-positive rates, making their

investigators more efficient and successful.

As important, insurance companies can reduce costs

and administrative tasks, allowing them to focus on

delivering a better customer experience.

It is important to remember that process automation

can happen gradually. Insurance companies

should evaluate processes that can easily leverage

automation to become more efficient. Once these

processes have been successfully streamlined, an

insurance company can expand to other areas.

When it comes to processing claims, onboarding

new clients, and renewing existing clients, one of

the biggest bottlenecks facing insurance companies

is people. A lot of work done by people is manual,

error-prone, and time-consuming. For example,

many claims are manually assessed individually,

which can create a significant backlog and, as a

result, unhappy customers.

New technologies have huge potential to

dramatically change how insurance companies do

business. Claims, for example, can be automated,

making processes more accurate, consistent and

faster. The claims determined to be legitimate can

be automatically approved while the small number

of complex claims can be flagged and assigned to

humans for review.

The search for legislative solutions.

According to Claims Journal, 116 pieces of

insurance fraud-related legislation were introduced

at the state level last year in the U.S.4 This resulted

in 33 new laws.

Some of the laws enacted include legislation in

several states to address the threat of insurance

fraud in worker’s compensation claims. An Alabama

law, for example, focused on contractors is aimed

2


Anti-fraud resources start to plateau.

Court decisions and legislation also take

time – more time than most insurers have to

effectively reduce costs and the effort

required to deal with fraud.

New laws can help but only after fraud has been

detected and investigated. Laws can also vary

widely by jurisdiction and won’t provide a consistent

way to handle fraud on an international level.

at “storm chasers” who file bogus claims following

severe weather. An Iowa law allows insurers to

pursue restitution from fraudsters, and Michigan

established its first state Fraud Authority.

In the meantime, courts in some states are

debating issues that could have a major impact

on fraud investigations, such as whether insurer

employees can be sued in bad faith for treble

damages and attorney fees.

During the 2018 Senate race in California,

insurance fraud emerged as an unexpected

campaign issue due to a reported 24% vacancy rate

of investigator jobs.5

When there aren’t enough people to review

suspicious insurance claims, the threat of such

crimes will rise and important services, such as

health-care programs, are affected. The same

things happen in the private sector too where

the challenges with investigator vacancy rates

means there are fewer eyes to identify potential

misuse, or that existing teams become ridiculously

overworked.

Even if insurance companies were overrun with job

applicants, traditional approaches such as tip lines,

provider/vendor watch lists, random audits on high-

value claims, and surveillance can’t keep up with

the pace of fraudulent activity.

As companies struggle to hire and retain

investigators and as existing teams are

overwhelmed by their workloads, existing studies

are unable to accurately determine the amount of

fraud that goes undetected.

Even when suspicious claims are identified,

false-positive rates are more than 90%, which

means most investigations eventually discover that

no fraud has been committed.

3


A.I. brings hope – but lots of confusion.

This is a waste of time and resources. Investigators

are chasing down the wrong cases while fraud goes

undetected.

Insurance companies need to explore how

technology can be leveraged to automate fraud

detection and make their investigations more

successful.

Given the impact of fraud, it is perhaps only natural

that something like artificial intelligence (A.I.) is seen

as a panacea to solve insurers’ biggest challenges.

The truth is more complex.

Part of the problem is that terms like “A.I.” are

loosely used in vendor marketing, including

instances where it is used interchangeably with

terms like predictive analytics.

Think of predictive analytics as using software to

create “rules” of what to look for in automated

claims processing, so if something looks suspicious,

an alert is generated.

Predictive analytics may come across as focused

on the future, but it relies entirely on historical

information. It can handle rank ordering of claims

more likely to have fraud or abuse, but it is not good

at record-by-record predictions.

Even at their best, A.I. tools that would be better

described as predictive analytics only address known

kinds of fraud experienced today.

4

Furthermore, predictive models need to be

refreshed or rebuilt over time and there is a lack

of a decisioning framework about what to do with

the predictive scores given that record-by-record

accuracy of predictive models is typically very low.

This isn’t to suggest A.I. doesn’t have real promise

for the insurance sector. Instead, it means this is the

time to become more educated about how true

A.I. works, and why it will change the way fraud is

identified, investigated, and ultimately reduced.

While predictive analytics is used to spot known

fraud types, true A.I. is best for finding new and

unknown types of fraud. Given that fraudsters have

a long track record of developing new approaches to

evade detection or operate in modes that didn’t exist

a few years ago, insurers will need A.I. to prevent the

growth in fraud costs.


You don’t have to be a technology expert to

understand what true A.I. – based on an approach

called reinforcement learning – can do for the

insurance industry.

Try to imagine the smartest, most self-directed

investigator the industry has ever known.

When someone has been reviewing suspicious

claims for their entire career, they have a good

sense of what emerged as fraud in the past. This

is based on their experience, as well as what

they have seen and learned from their peers, at

industry conferences, research, and colleagues.

This “investigator” behaves like predictive analytics

because it learns from what it learned in the past

and can be used for “known” kinds of fraud.

Now think of an ultra-efficient “investigator” (aka

reinforcement learning) who can recall with an

infallible memory all the fraud cases they have

encountered in the past AND can creatively think

through many other ways in which fraud could be

committed. This would see them develop a high

volume of “what-if” models beyond the capabilities

of a human being but do so without direct

intervention from a manager or other leader. As

important, reinforcement learning can also detect

unknown and new kinds of fraud.

In other words, reinforcement learning is “true” A.I.

that continually conducts tests to become smarter

in an autonomous way. The technology can analyze

100% of all transaction and claims data to discover

suspicious activity or abusive behaviour.

This includes detecting fraud at all entity levels

including the claim or transaction itself, people and

physical/virtual addresses associated with the claim/

transaction or persons, networks of individuals.

Reinforcement learning technology also detects

fraud before a claim is paid. This is important

because once a claim has been processed, litigation

is expensive, making it difficult to recover the

money.

What makes reinforcement learning so powerful is

that it uses trial and error to continually improve

over time. This makes it different from predictive

analytics, which only looks at historical information.

As important, reinforcement learning delivers

data-driven recommendations that help insurance

companies make faster and smarter decisions.

WHY REINFORCEMENT LEARNING IS A BETTER APPROACH TO FRAUD DETECTION THAN PREDICTIVE ANALYTICS


CONCLUSION

Insurance fraud is a problem that current approaches and tools

aren’t effectively tackling. It is becoming an increasingly bigger issue

that impacts an insurance company’s profitability and ability to stay

competitive.

As a result, insurance companies need to embrace innovative

technologies that discover and effectively deal with fraudulent

claims. And, as important, they need technology that automates

and streamlines many processes to drive efficiencies and return on

investment.

The use of A.I. and reinforcement learning is

changing the playing field. The technology

can significantly reduce fraudulent claims

by millions of dollars. And it can make the

people on the front lines more productive and

successful when it comes to battling fraud.

Sources:1 www.mpi.mb.ca/en/Newsroom/News-Releases/Pages/nr2018dec27.aspx2 www.insurancefraud.org/statistics.htm3 www.insurancefraud.org/article.htm?RecID=35664 www.claimsjournal.com/news/national/2018/11/19/287920.htm5 www.californiahealthline.org/news/shortage-of-insurance-fraud-cops-sparks-campaign-debate/


FOUR KEYS TO SUCCESSFULLY EMBRACE A.I.

The ability to analyze all your data delivers

specific recommendations about which claims

to investigate or to automatically process. By

detecting fraud more effectively and quickly, claims

payments can be dramatically reduced.

A $1 billion insurance company, for example,

could increase annual profits by $30 million to

$100 million. As well, Daisy can deliver ROI by

streamlining and automating processes to reduce

operating costs.

New technologies will allow them to focus on

claims that are highly suspicious, rather than

wasting their time on claims that turn out to be

valid.

The ability to automate processes delivers a

powerful combination of lower costs, better

customer service, operational efficiencies, and

higher revenue. There are a growing number

of A.I services and software so it is important

to understand which ones are “true A.I.” versus

products that aren’t really leveraging A.I. or

delivering predictive analytics.

Recognize that traditional approaches,

tools, and technologies aren’t as effective

as A.I.-powered technology.

Understand A.I.’s ability to deliver strong

ROI, simply by reducing claims payments

and increasing processing speed and

accuracy.

Convince your investigators that A.I.

will make them more successful and

productive.

Invest the time to learn about A.I.

technologies and trends.


HOW DAISY TACKLES INSURANCE FRAUD

Daisy uses different analytic methods: rules, predictions, social

networking, and peer analysis are combined using reinforcement

learning to reduce false positives and increase detection accuracy.

Using Daisy, insurance companies can:

• Save millions of dollars by avoiding fraudulent claims payments

before the money leaves the building. An insurance company

with $1 billion of claims annually can save $30 million to $100

million in claims payments.

• Lower false-positives rates from more than 90% to less than 50%

and as low as 10%. This improvement allows investigators to

focus on bigger, more suspicious activity, and fraud instances.

• Improve the ease by which potential fraud is identified. Daisy

makes your investigators more successful and efficient with

their time. You can do more with the same number or fewer

investigators. Daisy can reduce the time per investigation by

more than 80% by providing an investigator with data-driven

insight on what to investigate, centralizing all the information

in one place, and pre-populating with robotic automation tools

all the required data in the investigator’s case management

systems.

• Increase the number of claims that are straight through

processed, thereby increasing fraud recoveries and the total

volume of claims investigated.


HOW DAISY WORKS

We analyze 100% of an insurance company’s claims

data, initializing the system with several years of

claims and related data.

For known fraud, business rules and predictive

models are used. For unknown and new fraud types,

social networks, peer analysis, and fuzzy logic are

used.

Then, the risk from each method is mathematically

combined into a unified score, called the Daisy

Suspicion Index™, which indicates the risk associated

with each transaction, claim, person or social

network.

We develop social networks by first resolving

identities linking individuals who have different

system IDs (i.e. appear to be different people) but

who share names, birthdates, addresses, emails

bank accounts, vehicles, phone numbers, and other

attributes.

Daisy builds networks of individuals who should

not be related by identifying non-obvious links (i.e.

addresses, emails, phone numbers, bank accounts,

vehicles, phone numbers and other attributes)

between the individuals.

Non-obvious social networks are then peer analyzed

to identify the networks with outlying claim or

transaction behavior or networks containing

suspicious individuals. Specific recommendations

are made to best prioritize and target investigative

efforts to maximize ROI. Daisy tells you to either pay

a claim, investigate a claim, or don’t pay a claim.

As well, Daisy can increase the throughput of the

claims adjudication process by segmenting claims

into three buckets: claims that be automatically

processed, claims that require minimal review, and

claims that need a full review.

Daisy’s Theory of Risk™ considers the ripple effects

of denying payments. For example, if a claim is

denied, there could be a call into the call center

and this cost must be factored into the overall

ripple effect, as might the cancellation of a policy.

Conversely, a strong anti-fraud program might be

a competitive differentiator and selling point for

attracting net new business.

Based on the Daisy Suspicion Index™, our

system generates alerts delivered into our case

management tool, your existing client case

management tools, or robotic process automation

tools.


THE DAISY PROCESS

1 INGESTProvide us with at least two years of claims data and related dimensions.

3 SIMULATEUse multiple detection methodologies to identify and prioritize risk.

2 ANALYZEDaisy’s Theory of Risk™ finds the relationships between claims, people and networks.

4 DELIVERIdentify the risks associated with transactions in a secured web portal. Alerts are auto-generated.

5 MEASUREMeasure false positive rate and fraud cost recoveries and savings.


2300 Steeles Avenue West,Suite 240, VaughanOntario Canada L4K 5X6

[email protected]

www.daisyintelligence.com

tackling the growing problem of insurance fraud. · the alarming growth of fraud is making...

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