Analytics in Financial Industry
Data is the basic requirement for any decision. The amount and velocity of data is increasing
every day. It is available in on the premises as well as on the cloud. Now to extract the ...See more
Predictive analytics is used these days to predict the behaviour of the customer to augment
the customer’s internal & external experience. It not only recommends the best action to
drive the process but also automatically triggers it. It can coordinate lots of processes and
decisions thus acting as the brain of the organization. One major example in which data
analytics was used at a big stage is the 2012 US presidential elections. Mr. Obama employed
100 data experts to extract information from a huge database using predictive models to gain
a competitive edge. So an analytics driven campaign helped him in winning the elections. In
the report published by International Data Corporation it was mentioned that the spending on
business analytics services will be $89.6 billion in 2018 which was $51.6 billion in 2014.
This represents compound annual growth rate (CAGR) of 14.7.
Use of analytics in financial services industries
Economic slowdowns, increasing demands from customers and regulatory pressures are the
challenges that the financial industry is facing today. Also the stress on digital economy has
lead to explosive data growth. Due to increasing social media usage the consumer awareness
about the financial products is increasing today. All these factors have forced the financial
services industries to use data analytics to extract not just the useful but also actionable
insights from the data.
Analytics has brought a major change in the financial services industries in areas such as risk
& compliance, marketing, consumer and commercial banking, etc. So three broad functional
areas across which financial industries have adopted analytics are operations, risk and
1. Operations: The basic use of analytics here is to reduce the costs. This is done using
supply chain, workforce and IT operations analytics.
2. Risk: Analytics help to manage risks. This is done by fraud prediction, risk
assessment, regulatory compliance such as BASEL, CRAR etc and loss forecasting.
3. Marketing: Analytics is done to grow the business. It involves market segmentation
& sizing, market mix optimization and customer satisfaction.
In risk mitigation analytics help in making data driven decisions to help mitigate risk. Natural
language processing (NLP) is a technology that can analyze text and then digest the meaning
for it. It is now being used in the area of fraud detection as it can read employee’s mails and
also process and then digest new regulations. NLP can also access that which of the banking
areas or products can be at the risk of non compliance. Natural language generation (NLG)
technologies are also being used to generate AML suspicion activity reports. Analytics can
also be used to perform risk based pricing and scorecards because risk models which follow
the regulatory requirements can be made using tools. So analytics help financial services
industries to detect, prevent and mitigate risks in real time.
Customer analytics in financial services industry will involve:
1. Social media analytics: This will help in enhancing the online experiences for
customers. Also new trend emerging in social media analytics is tracking the
sentiment of customers on social media platform which can in turn help in developing
strategies for different products.
2. Customer lifecycle analysis: The most important application of analytics is customer
segmentation and targeting. Acquisition and churn analytics is also done to estimate
customer lifecycle value (CLV).
3. Campaign Management: Analytics can help to discover the opportunities to cross
sell and up sell. The marketing campaign’s effectiveness can be judged based on
return on investment (ROI). Analytics can also help in providing customer with real
time customized and personalized product offerings.
Almost every organization in financial services industry is using data analytics these days to
help them optimize their processes and serve their customers in a better way.
Royal Bank of Scotland (RBS) uses a big data analytics software SAS to improve its
customer service. The software helps them to analyze and visualize large amounts of data
thus the customers complaints are handles in a better way. The software tells them the errors
made by their staff. They are thus aware about what causes complaints and how they can be
resolved. So the key aspect of improving customer experience is taken care of by SAS. RBS
is targeting £100 million investment in developing analytics technology. The other use of
analytics they are targeting is to send automated text messages to inform the customer that
their cash is safe if they have left it accidentally after withdrawing from ATM. They are using
the technologies such as Cassandra and partnering with data driven start-ups like
JP Morgan uses data analytics in various ways. The investment banking giant uses the
Hadoop tool to leverage big data for analytics. They have massive amounts of data due to a
huge customer base. So Hadoop is used to process this data that include mails, social media
posts and other information that cannot be analyzed by conventional means. The Hadoop can
store bulk of data from various banking products. Along with the above mentioned uses
Hadoop helps JP Morgan to detect patterns, risks and find any opportunity available to make
money. Another platform used by JP Morgan is Sqrrl’s to help analyze and integrate cyber
datasets securely. Many other analytics tools like Equities Analytics, Correlation Analyzer
and Data Query are also used by JP Morgan.
Pitfalls that organizations face when using data for decision-making
A survey was consisted by Insight IQ in which 5000 employees of 22 global companies were
evaluated. The motive was to find employees who have strong analytics skills and are best
equipped to make decisions based on data analytics. It was found that only 50% of managers
and 38 % of employees fell into this group. So this survey clearly highlights the shortage of
skill set among employees when comes to decision- making using data analytics.
Another problem is that usually reliable information exists but it is very difficult to find. It is
like you have a library but no card catalogue. According to a survey done by Harvard
Business Review only 44% workers say that they are aware where to look for the information
they need. Companies should also try and increase the data literacy of their employees and
also incorporate information to decision making by providing those employees right tools.
According to Mckinsey director Tim McGuire companies face three major challenges while
using data analytics. First is to decide that which data needs to be used, next is having the
right capabilities to handle analytics and third is to transform operation by using the insights
gained. Lastly still there are many organizations which do not trust the data that they have.
So we can clearly see that data & analytics are at the centre of each organization. Especially
the financial services industries are employing lot of analytics in their processes. Analytics is
helping these organizations to serve their customers in a better way and also giving such
insights that were not possible with the conventional ways.
According to research report from Gartner the data analytics will take the centre stage when
huge data will be generated by embedded systems and as a result of which vast amount of
structured and unstructured data will be needed to be analyzed. Organizations will have to
filter the vast amount of data coming from internet of things and social media. They need to
then make sure that right information is delivered to right person at right moment. So,
analytics is slowly becoming deeply embedded everywhere.
Still there are the pitfalls that organizations face while using data for decision making. So
there is a need to design an end-to-end architecture to fulfil the requirements of growing
businesses. Organizations need to develop analytics skill in the employees instead of waiting
for someone else to extract useful insights from the data. Analytics should go viral. This can
be done by using a pragmatic approach to foster the development of analytics competency at
the point of data ingestion. Cloud should be incorporated in the end-to-end architecture. Also
the development of both professional as well as technical skills should be focussed upon to
make en-to-end architecture a success.
So, in the words of Tim McGuire, a McKinsey director analytics will define the difference
between the losers and winners going forward.
Added: 1 year ago
Quantitative techniques notes available by Prof. Pritam Ranjan IIM-I faculty. The below attatched notes are useful for all students who are studying management, Business Intelligence and Analytics. The documents include topics such as understanding probability, sample space, conditional probability, etc.