In this paper we will talk about how latest technology trends will help bankers be more informed on their customer behaviour, buying habits which will eventually help in predicting and analysing potential defaults especially in retail banking sector. The holy grail for such changing trends are Data and Analytics which will empower the banks with key customer insights using both Structured data which bank already has and unstructured data which is becoming possible to capture and analysis with the help of next-gen tools and system.

1. 5 Technology
Trends
In
Banking
Industry

2. In last decade, Risk Management in banks has seen a substantial change. After the
global financial crisis of 2008 the regulations that have emerged and the fines
which were levied has brought in new tsunami of changes on how bank look at Risk
functions. Huge investments are getting made on reviving and strengthening their
risk culture and involved of top management in key risk decisions.
In this paper we will talk about how latest technology trends will help bankers be
more informed on their customer behaviour, buying habits which will eventually
help in predicting and analysing potential defaults especially in retail banking
sector. The holy grail for such changing trends are Data and Analytics which will
empower the banks with key customer insights using both Structured data which
bank already has and unstructured data which is becoming possible to capture and
analysis with the help of next-gen tools and system.
Introduction

3. Technology
Trends Which
Will Reshape
the Bank Risk
Management
Functions
Banks have realized that in order to thrive in a market that has changed so
dramatically, they need to be able to improve their operational efficiencies, detect
fraud quicker and more accurately, model and manage their risk, and reduce
customer churn. To accomplish this, financial services firms are turning to big data
technologies and Hadoop to reduce risk, analyze fraud patterns, identify rogue
traders, more precisely target their marketing campaigns based on customer
segmentation, and improve customer satisfaction.
Reshaping the Bank Risk Management Functions:
1. Big Data is a Buzzword
2. Big Data Analysis
3. Predicative Analysis the Way Forward
4. Machine Learning and New Models are Erupting
5. Emergence of Blockchain and Bitcoin ‘Unicorns’

4. Trend-1
Big Data is a
Buzzword
Big data includes traditional or structured business sources of structured data, such
as the millions of daily transactional records from retail, financial, manufacturing,
or transportation/logistics industries and now there’s also non–traditional or
unstructured data that might come from social media sources like Facebooks,
Twitter, Youtube channels, Emails, Text messages, Voice calls , images , files etc
The German company Kreditech, that offers creditworthiness assessments of
private individuals, is an example of a successful niche player. The focus is on
location data, data of social networks, web analyses and data with reference to
online purchasing behaviour. Up to 10,000 data points per assessment are
considered by feeding in a “big data pool”.

5. Fig 1. Big Data Includes Traditional or Structured Business Sources of Structured Data

6. Trend-2
Big Data
Analysis
Banks grapple with huge quantities and varieties of data on one hand, and ever-
faster expectations for analysis on the other. Open source tools like JasperSoft BI
Suites, Pentaho Business Analytics helps producing reports from database columns
and is widely getting used in enterprise board meetings. Tableau & Qlik are leading
the way in Data visualization with both Desktop and server based BI tools. This will
be interesting space to watch with many new players trying to make their mark in
this space.
Numerous banks have already begun to implement big data projects. An example of
implementation in risk controlling is the UOB bank from Singapore. It successfully
tested a risk system based on big data, which makes the use of big data feasible with
the help of in-memory technology (data storage in the memory) and reduces the
calculation time of its total-bank risk (value at risk) from about 18 hours to only a
few minutes. This will make it possible in future to carry out stress tests in real time
and to react more quickly to new risks.

7. Trend-2
Big Data
Analysis
What Can Banks Do With Data Analytics?
Using flexible, sophisticated analytics, Banks can:
• Ask difficult questions, test multiple scenarios and
solve problems that could not be solved before.
• Generate highly accurate insights using more
variables, more complex analytical methods and more model
iterations than were previously not possible.
• Get the timely information you need to make
decisions in an ever-shrinking window of opportunity – so you
can take faster, better action on complex issues.

8. Trend-3
Predicative
Analysis the
Way Forward
Predictive analytics has been a part of most banks’ risk and
fraud management systems for some time -- either via third-
party identity verification and transaction risk-assessment
solutions or through internally developed big-data engines.
Big data has transformed the way organizations analyze and
optimize their internal and external business processes. For
banks, data analytics tools and technologies have been
particularly effective, especially for combatting risk and fraud.

9. Fig 2.The Structured Layout of Predictive Analysis

10. Trend-4
Machine
Learning and
New Models
are Erupting
This method improves the accuracy of risk models by identifying
complex, nonlinear patterns in large data sets which is difficult
prediction by bankers. Every bit of new information is used to
increase the predictive power of the model. Few banks which are
building MVC models using these techniques have achieved
promising early results. Since they cannot be traditionally
validated, self-learning models may not be approved for
regulatory capital purposes at this stage. Nevertheless, their
accuracy is compelling, and financial institutions will probably
employ machine learning for other purposes.

11. Potential Scenarios
Within Banking in
Which Machine
Learning Can
Heavily Contribute
a. Product Engineering - Knowing What to Sell, When, and To Whom
Creating perfect value propositions by combining different products, customer
behaviours and diverse channels is one of the major challenges in banking.
Applying machine learning to produce personalised product offering is key for
next generation banking. Propensity-to-buy a banking product is a critical KPI for
a banker to sell their products and services
b. Risk Management - Knowing the Creditworthiness of a Customer
Identifying a risk score of a customer based on his/ her nationality, occupation,
salary range, experience, industry he/she works for, credit history et. is very
critical for banks before even offering a product or service to customer. This risk
score is an important KPI for banks to decide on interest rate and other product
behaviours for the customer.

12. Trend-4
Machine
Learning and
New Models
are Erupting
c. Fraud Analytics
Another area banks face major challenge with - Frauds. Perhaps, one of the biggest
opportunities lies here in detecting fraud online and prevent by leveraging analytics
and machine learning to gain a holistic view of customers. identify patterns in data,
cluster information, and distinguish fraudulent activity from normal activity.
d. Treasury - CRM, Spot Transactions
CRM is very prominent in Retail Banking Space. When it comes to Treasury space
within banking, customer relationship management hardly exist. Treasury has a
diverse product palette such as FX, Options, Swaps, Forwards and more importantly
Spots. Having an online transaction by combining product sophistication of these, risk
aspects of customer, market and economy behaviour and credit history is almost a
distant dream for banks. Machine learning to combine a robust exchange rate pricing
supported by an instant risk sanity check and then placing a deal online – This is
taking it too far !!
Potential Scenarios Within Banking in Which Machine Learning Can Heavily Contribute
Potential Scenarios
Within Banking in
Which Machine
Learning Can
Heavily Contribute

13. Trend-5
Emergence of
Blockchain
and Bitcoin
‘Unicorns’
Blockchain - the distributed ledger technology underpinning bitcoin
cryptocurrencies – generated huge interest in 2015 and it is likely to
continue in 2016 as adoption broadens.
Many banks are already investigating how they can utilise blockchain
applications within uses outside finance too.
This is still early days however ,According to Jeremy Millar, partner at
Magister Advisors, M&A advisors to the technology industry, next year
will see the emergence of at least five bitcoin and blockchain businesses
with a valuation of more than $1 billion.

14. Conclusion
The problem with the future is that it is not always predictable; There is no models
of its workings. To develop these models, invention and innovation are needed.
For companies/organizations, the challenge is to rapidly adapt to and embrace
technology economics through the development of new financial models and
governance mechanisms. The biggest problem with the future is, those who figure
out what it is first will be the winners.
Risk management is one of the high-priority areas for banks using big data
analytics and predicative models. It will continue to remain so, however, as big
data analytics already provide powerful customer insights that help banks drive
top-line growth, maximize marketing ROI through micro-segmentation and
personalization, achieve greater customer centricity, improve loyalty and prevent
churn, we will see an increasing number of financial institutions taking advantage
of the big data solutions to grow their businesses and to gain a sustainable
competitive advantage.

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