This document discusses various uses of AI in banking, including: 1) Know Your Customer/Client (KYC) and fraud detection using machine learning to analyze transactions and communications. 2) Anomaly detection using time series analysis to flag suspicious transaction patterns in real-time. 3) Customer churn prediction analyzing complex customer behavior data to identify at-risk customers.

1. Examples of AI in Banking
By : Khawar Nehal
Muftasoft
http://atrc.net.pk/muftasoft
khawar@atrc.net.pk
Date : 15 July 2019

2. Agenda
Know Your Customer/Client (KYC)
Fraud Detection
Anomaly Detection
Customer Churn Prediction
Credit Risk Scoring
Anti Money Laundering
Phone CLI based Fraud
Automate New Account Openings
Consumer Loan Experience

3. Agenda
Mortgage Loan Experience
Banking Transactions
Financial and Market Information
Wealth Management
Customer Due Diligence
Streamline remote branch
Digital Mail room
International Trade
Compliance Information

4. Know Your Customer/Client (KYC)
Understanding the dynamics of your customer interaction with AI
Know Your Customer (KYC) is a key part of money laundering and anti-
terrorism legislation. The Customer Due Diligence (CDD) process requires
banks to file reports of suspicious activity. Almost two million such reports
were filed in the United States alone in 2017 according to a study by the
Royal United Services Institute for Defence and Security Studies — a U.K.
think tank. Failure to identify and file reports on suspicious transactions
results in billions of dollars in fines for banks. Investigators looking into
suspicious activity use a variety of tools including rules that flag frequent or
international transactions or interactions with offshore financial centers.
Unfortunately, with the volume and variety of transactions, rules-based
approaches are not flexible enough to capture new patterns and produce
large number of false positives that need to be reviewed.

5. Know Your Customer/Client (KYC)
AI is an ideal technology for finding anomalous patterns and identifying
areas of risk especially where there are a large number of items of
different types that need to be reviewed and potentially correlated.
Machine learning can be used to perform analysis of transactions and
can look for indicators of suspicious behavior including transactions
with dubious jurisdictions, suspicious companies or known parties. AI
can also offer better insights into transactions through analysis of both
structured and unstructured data. Natural Language Processing (NLP)
techniques allow AI systems to search through communications to find
additional signal including extracting metadata, identifying people or
companies referenced, and categorizing the intent or purpose of the
communication. All of these can help pinpoint suspicious transactions
and help investigators as they investigate transactions.

6. Fraud Detection
Stopping Fraud in its Tracks with AI
Fraud is a huge problem in the banking industry. In 2016, the
top 10 fraud types including wire fraud, card fraud, and loan
fraud accounted for $181 Billion in annual losses, and the
numbers are only increasing, according to fraud expert Frank
McKenna. Detecting and preventing fraud is a huge challenge
for banks given the large variety of fraud types and the
volume of transactions that need to be reviewed and manual
or rules-based systems can’t keep up.

7. Fraud Detection
AI can be used to analyze large volumes of transactions to find
fraud patterns and then use those patterns to identify fraud as it
happens in real-time. When fraud is suspected, AI models can be
used to reject transactions outright or flag transactions for
investigation and can even score the likelihood of fraud, so
investigators can prioritize their work on the most promising cases.
The AI model can also provide reason codes for the decision to flag
the transaction. These reason codes tell the investigator where they
might look to uncover the issues and help to streamline the
investigative process. AI can also learn from the investigators as
they review and clear suspicious transactions and automatically
reinforce the AI model’s understanding to avoid patterns that don’t
lead to fraudulent activities.

8. Fraud Detection
AI can be used to analyze large volumes of transactions to find
fraud patterns and then use those patterns to identify fraud as it
happens in real-time. When fraud is suspected, AI models can be
used to reject transactions outright or flag transactions for
investigation and can even score the likelihood of fraud, so
investigators can prioritize their work on the most promising cases.
The AI model can also provide reason codes for the decision to flag
the transaction. These reason codes tell the investigator where they
might look to uncover the issues and help to streamline the
investigative process. AI can also learn from the investigators as
they review and clear suspicious transactions and automatically
reinforce the AI model’s understanding to avoid patterns that don’t
lead to fraudulent activities.

9. Anomaly Detection
Finding Network Anomalies Faster with AI
Mobile applications are critical to many businesses today. For
credit card and banking companies, for example, mobile
applications represent a significant channel of interaction
where customer can review transactions, pay bills and
resolve support issues.
When application services are not available, customers use
more expensive call centers for support. With payment
applications, an outage means lost transactions, revenues
and increased customer churn.

10. Anomaly Detection
AI systems have been proven successful at detecting
anomalies in transaction volume data. This time series
process looks at expected data volumes based on historical
patterns. Upper and lower boundaries are also predicted
based on volume variation. This system is then used to
compare real-time transaction value to expected volume. This
real-time system allows network administrators to be notified
when transactions start to spike above or fall below these
boundaries so they can take action before an outage in
service.

11. Customer Churn Prediction
AI Helps Retain Valuable Banking Customers
Some financial services customers become quite valuable as
they generate fees on transactions and grow a portfolio of
business over the years including banking fees, credit cards,
home loans, personal loans and more. Simple churn analysis
uses rules based on known behaviors to identify potential
churn risks. Rules-based systems, however, are inflexible and
miss many customers who do churn and generate false
positives that end up giving expensive incentives to
customers who were not at risk to leave the bank.

12. Customer Churn Prediction
AI is a great solution for customer churn prediction as the problem
involves complex data over time and interactions between different
customer behaviors that can be difficult for people to identify.
AI can look at a variety of data, including new data sources, and at
relatively complex interactions between behaviors and compared
to individual history to determine risk. AI can also be used to
recommend the best offer that will most likely retain a valuable
customer.
In addition, AI can identify the reasons why a customer is at risk
and allow financial institution to act against those areas for the
individual customer and more globally.

13. Credit Risk Scoring
Personalizing Credit Decisions with AI
Banks and credit card companies use credit scores to evaluate
potential risk when lending money or providing credit. Traditional
credit scoring uses a scorecard method which weights various factors
including payment history, dept burden, length of credit history,
types of credit used, and recent credit inquiries. This traditional
method is based on broad segments and will deny credit to
consumers without considering their current situation or other
extenuating factors. Traditional methods may also give credit to
consumers, called churners, who are “gaming the system” and taking
out a large number of reward credit cards but are not profitable for
the issuers. For credit decisions there is also the additional regulatory
burden that banks and credit card companies must explain to the
consumer why they have been denied credit.

14. Credit Risk Scoring
AI is a great solution for credit scoring using more data to provide
an individualized credit score based on factors including current
income, employment opportunity, recent credit history, and ability
to earn in addition to older credit history. This more granular and
individualized approach allows banks and credit card companies
the ability to more accurately assess each borrower and allows
them to provide credit to people who would have been denied
under the scorecard system including people with income potential
such as new college graduates or temporary foreign nationals. AI
can also adapt to new problems, like credit card churners, who
might have a high credit score, but are not likely to be profitable
for the card issuer. AI can also satisfy regulatory requirements to
provide reason codes for credit decisions that explain the key
factors in credit decisions.

15. Anti-Money Laundering
Stopping Crime with AI.
Money laundering is a huge problem for the financial services sector.
According to the United Nations Office on Drugs and Crime, the
estimated $2 trillion is “cleaned” through the banking system each year.
Fines for banks who fail to stop money laundering have increased by
500X in the last decade to more than $10 Billion per year. As a result,
banks have built large teams of people and given them the time-
consuming task of finding and investigating suspicious transactions which
often take the form of numerous small transfers within a complex network
of players. Investigation teams have used rules-based systems to find
suspicious transactions, but the rules quickly become outdated and
produce large numbers of false positives that still need to be reviewed.

16. Anti-Money Laundering
AI, especially time series modeling, is particularly good at looking at
series of complex transactions and finding anomalies. Anti-money
laundering using machine learning techniques can find suspicious
transactions and networks of transactions. These transactions are
flagged for investigation and can be scored as high, medium or low
priority so that the investigator can prioritize their efforts. The AI can
also provide reason codes for the decision to flag the transaction.
These reason code tell the investigator where they might look to
uncover the issues and help to streamline the investigative process.
AI can also learn from the investigators as they review and clear
suspicious transactions and automatically reinforce the AI model’s
understanding to avoid patterns that don’t lead to laundered money.

17. Phone Identification
There was once a time when people trusted the number that
showed up on their Caller ID. Phone companies charged
extra for the service. Even banks allowed you to activate your
credit card just by calling from a registered phone number.
Today, that is no longer the case.
Caller ID (CLI) and Automatic Number Identification (ANI)
were originally designed as systems to be used internally by
the phone companies. As such, they didn’t need any real
security. As they emerged as consumer facing tools, they
never developed the security features that we expect today.

18. Phone Identification
The result is that spoofing Caller ID data, or ANIs, is very easy. A quick
Google search turns up pages of articles on how to spoof a number.
App stores are full of easy to use apps that enable spoofing. One
smartphone app, Caller ID Faker, has over 1,000,000 downloads.
Adding to the problem is the fact that in general, Calling Liner ID
spoofing is completely legal. Though it is always illegal to use CLI
spoofing for fraud or threatening messages, it is perfectly legal to
spoof a number as a friendly prank, or as a helpful business practice.
(Think doctors on call who don’t want to give out their cell phone
number.) While it might be fun to spoof a CLI in a prank call to your
friend, too often fraudsters are the ones disguising their numbers to
hide their criminal activity.

19. Phone Identification
System are available that track phone fraud activity and
trends. We have found that CLI and ANI spoofing is the most
common technique used by phone fraudsters. In addition,
more than half of the caller ID spoofing attacks cross
international boundaries, meaning they are almost impossible
to track down and prosecute.

20. Phone Identification
Consider the case of one attacker, known to Pindrop
researchers as “Fritz.” This fraudster is likely based in Europe
and works alone.
Fritz is in the business of account takeover. He calls financial
institution call centres, impersonating legitimate customers by
spoofing ANIs, and socially engineers the bank into
transferring money out of an account.
In one four month period, we found that Fritz had targeted 15
accounts. We estimate that he has netted more than
£650,000 a year for at least several years.

21. Phone Identification
While there is no technology that can prevent CLI spoofing, it
is possible to detect these calls. The key is to detect
anomalies between the information being sent over the Caller
ID and the actual audio characteristics of a call.
This technology analyses the audio content of a phone call,
measuring 147 characteristics of the audio signal in order to
form a unique fingerprint for the call. It can identify the region
the call originated from and determine if the call was from a
landline, cell phone or specific VoIP provider. These pieces of
information provide an unprecedented level of insight into
caller behavior.

22. Phone Identification
So, if a Caller ID says a call is coming from London, but the
phoneprint of the call shows that the individual is calling from
1,000 miles away, it should be a red flag for anyone running a
call centre that the caller has malicious intent.

24. Phone Identification
One recent fraud attempt thwarted by these tools happened
on a Saturday night, a time when most call centre employees
are not at their most vigilant. The caller asked to transfer
£63,900 from one bank to another. The Caller ID matched the
phone number associated with the account, and the caller
knew all the answers to the identity questions the agent
asked.

25. Phone Identification
These solutions are already protecting calls to top banks,
financial institutions, and retailers. The platform is a
comprehensive solution designed to protect the entire call
system: inbound, outbound, live, recorded and in the IVR,
customer-facing and employee-facing interactions. It uses the
information from the phone to create a highly accurate and
highly actionable risk score for each call, which has allowed it
to catch more than 80 percent of fraud calls within 30
seconds after the call has been initiated.

26. Automate New Account Openings

27. Automate New Account Openings
Automate New Account Openings to Enhance the Customer
Onboarding Experience
New account openings are unavoidably information-intensive:
customer data pours in from multiple channels and devices,
and in multiple formats. If your processes are manual, you face
a threefold disadvantage—rising costs due to operational
inefficiencies, customer dissatisfaction resulting from lengthy,
cumbersome tasks, and risk of regulatory noncompliance fines.
This is the challenge facing the more than 70% of banks that
do not have an end-to-end digital onboarding process.

28. Consumer Loan Experience

29. Consumer Loan Experience
Create a Fully Digital Consumer Loan Experience from
Origination to Closing
Consumer lending has a reputation for being paper-intensive,
and many borrowers dread the mounds of paper forms
awaiting their signature. But technology and new legislation
are changing both the customer experience and operational
efficiency with paperless processes, digital signatures, mobile
capabilities and back-office automation and compliance.

30. Mortgage Loan Experience
Create a Fully Digital Mortgage Loan Experience from
Origination and Closing to Servicing
Consumer mortgage lending has a reputation for being
paper-intensive. An average of 500 documents are generated
per application—and that doesn’t even count loan servicing
documents. Modern borrowers (and brokers) don’t want to
deal with mounds of paper, and considering the risks of lost
documentation, costly data entry errors and compliance
violations, neither should you.

31. Banking Transactions

32. Banking Transactions
Accelerate Banking Transactions and Empower Customers
Through Self-Service Capabilities
Today’s mobile-first consumers are unlikely to have the
patience to stand in line at a branch, enter information
repeatedly or wait days for an application approval. However,
the systems running many banks weren’t designed for the
speed and intuitive self-service options required to satisfy
customers.

33. Banking Transactions
Accelerate banking transactions and customer onboarding by
empowering your customers to open an account or apply for
a loan via their method of choice.
Customers can use their mobile device to snap a photo of an
ID or document, a check for deposit or a card for account
funding, as well as use a tablet at a branch kiosk.
By embracing a digital self-service/assisted-service model via
a single, open platform, you build customer loyalty while
driving revenue.

34. Financial and Market Information

35. Financial and Market Information
Gain a Competitive Advantage with Real-Time Financial and
Market Information
While traditional Business Intelligence (BI) and Information
Management data is critical for making investment decisions,
your competitive advantage lies in integrating real-time web
and proprietary data on market and customer trends and
leveraging investment analytics to uncover insights even in
typically opaque markets.

36. Financial and Market Information
A wealth of information on the web—from corporate actions
and operational data to macro news—provides up-to-the-
second information and metrics used to support predictive
trend analysis, but manually collecting the quantity and
quality of information you need to make smart investment
decisions is nearly impossible.

37. Financial and Market Information
Automate and scale the acquisition of financial data and
equity research with an integrated platform that feeds real-
time data directly into your business intelligence and analytics
solutions. Deliver thoughtful, insightful and differentiated
research and make sound and timely sell-side and buy-side
investment recommendations. The unprecedented accuracy,
quality and timeliness of research that supports big data,
smart data and complex research initiatives will eliminate
time-consuming manual work and ultimately enable more
profitable investment decisions on behalf of your company
and clients.

38. Wealth Management
Streamline Onboarding and Wealth Management Processes
for High-Net Worth Clients
Wealth managers and investment firms hoping to attract high-
net worth clients face time and cost pressures—both from
their potential customers and from self-service and direct-to-
consumer (D2C) platforms seeking to gain market share. It
can take 41 days for a firm to onboard a high-net worth client;
this is problematic for digitally savvy consumers who have no
patience for delayed time-to-revenue.

39. Wealth Management
Reduce client inertia and automate your onboarding and
customer communications processes through an open,
flexible platform that allows high-net worth customers to
engage with your business via the channel of their choice.
Eliminate information silos and drive efficiencies, while
helping your firm avoid the financial and reputational
consequences of regulatory noncompliance.

40. Customer Due Diligence
Take the Complexity out of Customer Due Diligence Compliance in
Banking
Financial institutions must find better ways to comply with
increasing regulations to avoid fines and damage to their
reputation and bottom line. The challenge is three-fold: meet the
compliance requirements of Customer Due Diligence (CDD),
including Know Your Customer (KYC) and Anti-Money Laundering
(AML) checks, while delivering an omnichannel customer
experience and process efficiencies. Failure to do so can have
serious consequences; for example, Deutsche Bank was fined
£188m for serious anti-money laundering control violations.

41. Customer Due Diligence
Eliminate manual task burdens on your employees by
deploying robotic process automation (RPA) to reduce errors,
costs and timelines, and increase customer satisfaction. Drive
even greater value via an agile, open digital transformation
platform, and take your compliance efforts to the next level.

42. Streamline remote branch
Streamline remote branch capture and create a superior customer
experience
For banking customers, the ease of doing business is a core driver
of satisfaction. When customers come into a branch, they expect
your technology to keep pace with the digital revolution. When you
have to send documents to a central location for scanning,
extraction, verification and routing to business processes, you
delay everything from loan closings to funds availability.

43. Streamline remote branch
Transform your customers’ experience and your operational
efficiency with a branch and teller capture solution that will
enable your financial institution to keep pace with the rapidly-
changing digital banking environment. Not only can you
provide superior customer service with on-the-spot
processing, faster access to funds and proactive document
verification, but your operating costs will be lower with digital
delivery and a secure audit trail for compliance.

44. Digital Mailroom
Automate Banking Business Processes with the Digital Mailroom
While the optimum flow of information is one of the best ways to
increase profitability and improve service levels within your bank,
the different document types and the sheer volume you receive
likely create challenges in gathering and disseminating information
quickly and accurately.
Imagine your bank’s underwriting department is waiting for a
customer document to approve a mortgage loan.
The customer sent the fax successfully, but it cannot be located; the
results of this inefficiency are potential lost revenue and risk of
regulatory non-compliance fines.

45. Digital Mailroom
Deploy digital mailroom automation software to streamline the
capture of incoming mail—including paper, email, fax, or at
the Point of Origination (MFP, web portal or mobile/tablet
device)—and deliver structured electronic information to your
bank’s business systems.
Track, review and modify information at any point in the
process via analytics dashboards; digital mailroom
automation software can enhance decision-making based on
real-time information to increase throughput and revenue
generation.

46. International Trade
Optimize International Trade Management by Automating
Financial and Regulatory Processes
Post-trade services are important after any trade, whether the
parties trade over an exchange or over the counter (OTC), and
whether the trade involves domestic or international securities.
And since markets and prices move quickly, transactions must
also be executed quickly, which raises your risk of costly
errors.

47. International Trade
Automate your global trade management, including financial
message handling, transaction matching and reconciliation, to
speed processing, reduce risk and drive efficiencies. Whether
you are the buyer or seller of securities, you can improve
trade process transparency, monitor performance and handle
exceptions quickly, while ensuring data security and
compliance. Deploy the solution together with a digital
transformation platform for even greater operational benefits.

48. Compliance Information
Automatically Aggregate and Integrate Compliance Information
for Banking
Today’s financial services organizations are struggling to comply
with regulations including Customer Due Diligence (CDD), Know
Your Customer (KYC) and Bank Secrecy Act (BSA)/Anti-Money
Laundering (AML). A survey of more than 100 senior banking
officials across Europe and the U.S. found that one in five banks
are significantly increasing spending around compliance
requirements; a key challenge is that much of the data needed
to ensure compliance resides outside your bank, making it
difficult to aggregate and integrate into your internal processes.

49. Compliance Information
Ensure the consistent application of banking business rules
and regulatory compliance standards through an integrated
process automation solution. Automatically acquire, enhance
and deliver the precise data required from any internal or
external source. You will save your bank time and money by
avoiding repetitive tracking and reporting activities, and
reduce your risk of noncompliance fines.

50. Examples of AI in Banking
By : Khawar Nehal
Muftasoft
http://atrc.net.pk/muftasoft
khawar@atrc.net.pk
Date : 15 July 2019