The impact of churn within the financial services industry is striking. BCG research found that attrition affects 30% to 50% of a corporate bank's client base and spans all products and segments.

1. CHURN IS A TOP REVENUE-LEAKAGE PROBLEM FOR BANKS:
IS DEEP LEARNING THE ANSWER?
By Murali Mahalingam, Chief Strategy Officer, Razorthink Inc.
Presented By

2. The impact of churn within the financial services
industry is striking.

3. BCG research found that attrition affects 30% to
50% of a corporate bank's client base and spans
all products and segments.
Corporate banks annually lose 10% to 15% of
gross revenues to attrition.

4. The BCG analysis indicated that full relationship
exits account for an average of 1% to 2% of gross
revenue loss.

5. Other forms of attrition, such as partial
defections (clients close a few accounts or stop
buying some products) and volume cuts (clients
rein in the amount of business conducted across
the portfolio as a whole) do the most damage.

6. According to the study, relationship exits that are
more gradual account for 9% to 13% of gross
revenue loss per year.

7. The numbers are similar on the retail side. The
Accenture Banking Customer 2020 Survey
found that over the last five years 18% of retail
bank customers switched providers completely
and 27% added new providers.

8. The study revealed that over the same period the
combined switch rates (customers who switched
completely from one bank to another or partially
by adding new providers to existing ones)
increased globally by 8%.

9. Each point of attrition reflects as much as 2% of
net income loss for banks.

10. When one considers that financial institutions
spend an average of as much as $300 to attract
each new retail customer — and more to attract
commercial customers — the cost of replacing
customers can quickly add up.
And there's a distinct possibility that many of
those customers won't last.

11. Thus, the process of replacing customers
continues in a repetitive cycle, draining revenues
and operational efficiency.

12. Customer and Company
Factors

13. The impact of customer churn on financial
services is considerable for many reasons. Banks
not only contend with traditional life
consequences that result in churn (such as death,
divorce and displacement), but also an
increasingly competitive landscape altered by
mobile technologies, digital transformation and
greater customer expectations.

14. The advent of electronic banking, online virtual
banks and non-bank providers has de-
personalized the banking experience, resulting in
less loyalty and more attrition.

15. Most banks have enough data to reduce the
instance of churn which, when decreased even
5%, can boost net profits as much as 80%.
However, many fail to either analyze their data
accurately or quickly enough to reduce attrition
rates.

16. Employing expensive data scientists and classic
machine learning techniques can take months to
analyze the bevy of data sources required to
determine the feature characteristics for churn
— during which time customers may leave.

17. The good news: Artificial
intelligence data science is now
practical

18. The BCG research also revealed that by
employing predictive analytics and arming
relationship managers with data-driven insights
and enablers, corporate banks can reduce total
attrition by 20% to 30% — a result that would
nearly double most banks' average revenue
growth.

19. But breaking the attrition cycle requires more
than simple awareness of customer behavior. In
today's world of digital transactions,
understanding customer behavior requires
analysis of big data — which includes
transaction data, customer profiles and customer
micro-segmentation data — to understand
leading indicators for future customer behaviors.

20. these data sources in real time at scale is now
possible using artificial intelligence (AI) data
science, which readily parses through these data
elements to detect the features that can
accurately predict churn and prescribe how to
prevent it from occurring.

21. Using AI to drive customer analytics empowers
financial institutions to recognize complex
patterns faster — in real time.

22. By using deep learning AI, banks can uncover
previously unknown patterns and figure out
what those patterns mean in regard to the
customer experience.

23. This ability creates opportunities to pinpoint
customer needs and wants, anticipate their
actions, and prevent customer churn by
surprising customers with custom offers,
experiences and service.

24. Organizations can take specific actions that
positively impact the individual customer —
creating a unique, personalized experience that
builds loyalty.

25. Why all the hype about deep
learning?

26. Deep learning is being positioned by many
experts as the future of artificial intelligence.

27. Most people are aware of its enhanced
capabilities to recognize patterns in astounding
amounts of big data over lengthy time periods.

28. Those that follow AI are familiar with the high
accuracy rates of its predictions based on those
patterns. But there are actually three advantages
of using deep learning:

29. 1. Superior accuracy. Compared to classic
machine learning, deep learning has a much
higher rate of accuracy — up to 98% for image
recognition as demonstrated in the 2016
ImageNet Large Scale Visual Recognition
Challenge.

30. 2. Complex data doesn't break it. Deep
learning can encompass more variables than
classic machine learning and is ideal for the
numerous data sets required to understand
financial customer behavior.

31. 3. Identifies what you don't know.
Most importantly, deep learning techniques are
responsible for their own "feature detection," the
process of ascertaining which characteristics of
data are relevant to a particular use case such as
churn.

32. Feature detection in conventional machine
learning requires humans to predetermine the
features and relationships involved.

33. Deep learning algorithms can identify new
patterns or anomalies in raw data, therefore
helping banks avoid “blind spots."
Thus, organizations can readily understand what
factors are affecting churn for customers, micro-
segment them according to those factors, and
deliver strategies to retain them.

34. Empowering banks
with deep learning

35. Because most banks maintain extensive customer
profiles and log millions upon millions of
transactions, banking operations are ideally
suited for deep learning.

36. It can determine which customers are likely to
churn and why, and provide insights into which
campaigns proved effective in mitigating churn
— all for a fraction of the amount banks typically
lose to churn each year.

37. Deep learning can predict, at a granular level,
the customers that will cancel subscriptions, stop
using a product or service, and/or respond to
competitive offers.
By identifying churn in advance, companies can
take actions that will change outcomes.

38. Deep learning can be a powerful tool for data
scientists, IT teams and the banks for which they
work.

39. It can serve to reduce the manual labor involved
in analyzing financial data for customers,
enabling banking professionals to focus on long-
term, value-adding strategies for reducing churn
and increasing customers.

40. Deep learning methods give banks new insight
from their data and fuel the prescriptive analytics
to minimize customer churn, increase employee
efficiency and drive operational success.

41. About the author
Murali Mahalingam is Chief Strategy Officer for Razorthink Inc.
You can reach him at murali.mahalingam@razorthink.com.
Razorthink accelerates digital transformation for banks, financial services and
insurance companies through deep learning intelligent systems.

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