Big data and machine learning analytics can help banks and insurers combat fraud and reduce losses from hacking. Techniques like anomaly detection can flag suspicious transactions to identify issues before they become major problems. While not a perfect solution, these tools provide insights and alerts that go beyond traditional methods. Their use can decrease fraud incidents and strengthen organizations' technological defenses, but hacking will still remain an ongoing challenge given human factors and hackers' continuing innovation.

1. Big Data Analytical Driven Fraud Detection for Finance-
Banking and Insurance

2.  Customer data security is one of the biggest challenges that banks the world over face. A
few numbers show how significant this risk is:
 The Nilson Report estimates that in 2016, losses topped USD 24.71 billion. That represents a
12% increase over the previous year.
 According to a report from Javelin Strategy, there's a new identity theft victim every two
seconds, and many of the incidents involve credit cards.
 ACI Worldwide (an electronic payment systems company) estimates that 46% of Americans
have had their card information compromised at some point in the past 5 years.
 Approximately 65% of the time, credit card fraud results in a direct or indirect financial loss
for the victim.
 Among victims who reported direct financial losses, the average was $7,761 and the median
was $2,000 per victim. This is compared with those who suffered indirect losses with an
average of $261 and median of $10.
 23 frightening credit card statistics: Feb 1, 2017; Rebecca Lake; Available at:
https://www.creditdonkey.com/credit-card-fraud-statistics.html

3.  Fraud accounts for 5-10 percent of claims costs for U.S. and Canadian insurers.
Nearly one-third of insurers (32 percent) say fraud was as high as 20 percent of
claims costs;
 57 percent of insurers predict an increase in personal-property fraud by
policyholders. Around 58 percent say the same for personal auto insurance, and 69
percent expect a rise in workers-compensation scams;
 61 percent predict an increase in auto-insurance fraud by organized rings, and 55
percent predict an increase workers-compensation scamming;
 About 35 percent say fraud costs their companies 5-10 percent of claim volume.
More than 30 percent say fraud losses cost 10-20 percent of claim volume;
 Detecting fraud before claims are paid, and upgrading analytics, were mentioned
most often as the insurers’ main fraud-fighting priorities; and
 One-third of insurers don’t feel adequately protected against fraud. (FICO, August
2013)
 Insurance fraud statistics: Available at: http://www.insurancefraud.org/statistics.htm#2

4.  Insurance companies lose an estimated $30 billion per year in
insurance fraud costs that have to get passed on to bill-paying
consumers.
 The most common types of insurance fraud are:
 1) Stolen car
 2) car accident
 3) car damage
 4)health insurance billing fraud
 5) unnecessary medical procedures
 6) staged home fires
 7) storm fraud
 8) abandoned house fire
 9)faked death
 10) renter’s insurance
 Insurance fraud statistics: Available at: http://www.insurancefraud.org/statistics.htm#2

5.  Customer data security is one of the biggest challenges that banks the world over face but
Pakistani banks have now become even more vulnerable. A few quotes highlight why:
 “We not only face threats from hackers who skim ATMs or manipulate online accounts just for
swindling money, but also from organised hacking groups whose objectives are wider,”- the
head of a local bank.
 “Pakistan’s entire security establishment is walking a tight rope after entering into the CPEC
[China-Pakistan Economic Corridor]. Foreign powers are making every effort to embarrass the
country. We need to thoroughly investigate the real motives behind the recent skimming in the
light of previous bank data stealing incidents in which some Chinese nationals were involved,” a
well-placed source in the FIA
 Over the years, the use of ATMs has been growing rapidly in Pakistan. According to SBP
statistics, about 110m ATM transactions took place in just the nine months from July 2016 to
March 2017, with the total value of these transactions exceeding Rs960 billion. As banks
continue to encourage their clients to use ATMs and as people experience the benefits doing
so, these numbers are only poised to grow.

6.  In the current spree of ATM skimming, 296 customers of HBL have so far confirmed being
defrauded, an aggregate loss of PKR 10.2m, implied a press release issued by the State Bank
of Pakistan (SBP) on Dec 5. The number of bank accounts affected, though, is around 600,
according to newspaper reports.
 Apart from this, several such cases have been reported from Dolmen Mall, Karachi. Reports
also surfaced of a similar cyberattack in Islamabad. Banks including HBL responded by
blocking users’ ATM cards as a precaution against further loss.
 Rising Prevalence of ATM Fraud; Dec 11,2017; Dawn; Available at:
https://www.dawn.com/news/1375856
 Beware- Hackers are going after ATMs in Pakistan: Salman Siddiqui, Dec 3, 2017: The Express
Tribune: Available at: https://tribune.com.pk/story/1574702/2-beware-hackers-going-atms-
pakistan/

7.  The National Accountability Bureau (NAB) on Saturday arrested 23 accused officials
of State Life Insurance Corporation, including a regional manager, in a scam of over
Rs 100 million related to bogus policies. According to a NAB spokesman, the accused
caused the heavy loss through bogus insurance policies by opening 113 bank
accounts and withdrawing cash against over 430 cheques. He said the accused
officials facilitated fake policies to around 90 individuals who had never entered into
any policy with State Life Insurance. He said a local government councillor was also
among the arrested accused.
 Inflated health claims, stolen cars, money laundering and fraud through life insurance is
common but not properly analyzed and quantified. Central databases need to be made by
SECP regulator too.
 NAB arrests 23 officers in PKR 100 million insurance scam; April 17,2017; The News; Available
at:https://www.thenews.com.pk/print/199063-NAB-arrests-23-officers-in-Rs-100m-
insurance-scam

8. Culture
Level
Soft Facts
Organizational
Level
90% of the
problems
caused by
hacking
remain
undetected
and hidden
Hard Facts
Only 10% of the
problems caused by
hacking are brought
onto the surface
Top leadership Board of
Directors driven
initiative is key to
establishing
comprehensive Cyber
crime division in the
bank.

9. Availing latest technology
and analytics
Handling situations
correctly by the bank
and insurer
Big Data
Neural Networks
Machine Learning
Anomaly Detection
KPIs holistic; key
metrices
Clustering
Deep Learning
Fraud Analytics
Customer support
and awareness
Forensic IT
Handling situations
holistically
PR and Customer
perception handling
Preparing
contingency plans
for hacking
Holistically combating fraud
Deep Learning is based on neural networks
which mimic how our brain and neurons
work.
Big Data and Machine Learning Analytics

10. Big Data and Machine Learning Analytics
Being hacked is an inevitable fact of life and normal way
of doing business now. What matters now is how we
handle the crises when it occurs, and how much pre-
emptive preparations we take to minimize hacking
attempts. It’s important to remain at level with hackers on
technology and to utilize new technologies so that we
remain at the forefront of all cyber issues
Utilizing big data and Machine Learning (ML) is one of the
way to remain updated and gives us a strong deterrent
mechanism with which to minimize cyber hacking
attempts.
Gain insights and alerts from machine learning techniques
that go far beyond static thresholds and traditional
dashboards. Predict issues before they become major
ones.
Flag suspicious transactions. This is when some fraud
can’t be proven or money laundering cant be proven but
it is still suspicious. decrease fraud incidents and
increase your technology arsenal against hacking
efforts through machine learning.

11. Anomaly Detection
Machine learning algorithms learn the normal behavior of your business data in order to identify and
alert on anomalies and on what is abnormal. Anomalies aren’t categorically good or bad, they’re just
deviations from the expected value for a metric at any given point in time.
You can’t correctly attribute a specific anomaly to the underlying business incident if you don’t know
about anomalies to begin with (both good and bad anomalies). And that’s one of the main reasons
companies need anomaly detection: to get accurate feedback on the effectiveness of business
initiatives so that money and manpower can be utilized much more efficiently and to greater impact
for a company’s bottom line. Anomaly detection can point to positive business incidents as well as to
potential disasters.

12. The Silver Lining; but not the
magical cure for everything
– It's important to specify what machine learning is not:
– Big Data and ML is not a magic bullet to cure all hacking. There is no such thing
as ‘unhackable’. Even the best organizations and the most secretive ones like
CIA, NSA, Facebook, Microsoft, Uber get hacked.
– There are always human factors in place as well.
– Hacking won’t stop; it will only get worse as technology increases. As we
modernize over the future digital trends and hence financial consequences of
hacking will only increase. Digital trends are quickly becoming mainstream like
more online transactions, availing crypto-currencies like bitcoin, quantum
computers and is only projected to exponentially change our lifestyles.
– When there is a will, there is a way; hackers will continue inventing and finding
out new ways to exploit our customers. Banks must stay updated on technology
to minimize hacking to safeguard customer trust in their organization.
– Even when quantum computers will become a reality, there won’t be any
internet or online service that is ‘unhackable’. Classical cryptography will
become obsolete yes but will be replaced by quantum cryptography and new
ways of to hack and stop hacking.