5 Technology Trends

InBanking Industry

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

Technology Trends

Which Will Reshape the Bank

Risk Manageme

nt 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’

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”.

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

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.

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.

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.

Fig 2.The Structured Layout of Predictive Analysis

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.

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.

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

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.

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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