Financial institutions use data analytics to gain insights from customer data to improve operations. Data is collected and analyzed to understand customer habits, market trends, and inefficiencies. These insights can then be used to increase revenue, retain customers, and boost profits. Implementing analytics requires identifying use cases, measuring analytics impact, finding talent, and considering technical requirements.

1. Data Analytics in
Finance
www.positivenaick.com
Finding the secret sauce to predict
customer trends in finance.

2. Overview of Analytics
Financial institutions exist not just by providing a safe place for customers to save their money, but
also provide avenues where customers can invest their money and procure loans for their own
expenses. A healthy finance industry is a good sign of a strong national economy. When money
flows freely from and to consumers, it enriches all other industries.
That’s why it’s important to remember that financial institutions need to operate at peak efficiency,
ensuring great returns for themselves as well as their customers, to ensure that every other cog in
the economy’s machine runs smoothly.
Data analytics works by tapping into the rivers of data financial institutions are already collecting
and storing, and adding new methods for data collection. Once all this data is collected, we can use
computers to analyze and find patterns in data.
These patterns give us insights into customer habits, market trends, operational inefficiencies, and
bottlenecks. Using these insights, we can find new methods and strategies to improve revenue,
retain customers, and increase profits.
1
Credit Risk
Fraud, AML
Liquidity
Risk
Default
Management
Capital
Allocation
Analysis
Credit Loss
Provision
Net Interest
Margin
Variance
Structured
Finance
Analysis
Liquidity
Analysis
Customer
Profitability
Cross Sell
Analysis
Customer
Acquisition
Customer
Behavior
Analysis
Customer
Loyalty
Campaign
Analysis
Call Center
Analytics
Channel
Profitability
Location
Profitability
Product
Profitability
Customer
Life Time Value
Profitability RiskRelationship Marketing
Financial Analytics
Funds
Marturity
Analysis
Collections
and Credit
Exposure
Asset & Liability Management

3. 2
Financial Risk Modeling
The need to weigh financial
risks is important, since it can
show which customers can be
given loans without worry of
repayment. Additionally, it can
also look into investment
opportunities, to gauge
whether a particular
investment is likely to yield
better returns compared to
another.
Business Need
Here, the expected outcome
is to categorize customers
and investments into two
primary buckets, whether
they’re high-risk or low-risk.
Based on this, the financial
institution can choose at their
discretion about whether to
lend or invest.
OutcomeApproach
To gauge financial risk, we can
look at customer financial
records, their banking history,
credit history, and repayment
histories to find out whether a
customer has enough
repayment ability to safely
provide a loan.
Additionally, a similar
approach can be used to
gauge risks in investment
opportunities based on their
past performance, as well as
their current statuses.
In the past, risk modeling used to be done manually, on a qualitative basis. Although this method
does have some merits, it has the potential to miss out on some key details in some cases, falsely
identifying risky investments as safe. It can even mis-identify safe investments as risky.
Financial
Data
Economic
Data
The BCM
Market
Risk
Model
Outlook
Favorable
or
Unfavorable
Monetary
Data
Financial risk model

4. 3
The expected outcome here
is to have reports that
provide solid information on
why a specific investment or
lending failed, thereby
helping give more
information to the financial
institution about which
investments should be
prioritized and which should
not be accepted.
Outcome
Diagnostic Analytics
By finding out at-risk
investments and customers,
financial institutions can
decide with concrete
knowledge that some
investments and lending are
not worth the risk, reducing
the losses incurred by the
institution.
Business Need
Diagnostic Analytics looks into data to understand the basics of what happened. This can be for a
mutual fund that didn’t yield any promising returns, or a set of customers who couldn’t pay back
their loans.
Approach
By looking into past
repayments for customers, or
financial records for
companies seeking
investment, diagnostic
analytics will take past and
current financial performance
into account and produce a
report on why a certain
investment or loan failed. This
can be used for future lending
activities.
Causal / driver
analysis
Explanatory
statistical
models
Enthusiastic
consumption
of findigs /
insights
Detailed
dashboards
Implementing
recommenda
-tions
can be
difficult
Diagnostic
Analytics

5. 4
Customer Analytics
By analyzing customers, it is
possible to discern high-value
customers from high-risk
customers. High value
customers can be offered more
products to increase their
customer relationship with the
financial institution, while
high-risk customers can be
denied or charged higher
interest rates on loans.
Business Need
By creating different buckets
for customers, financial
institutions can offer more and
better products to high-value
customers to retain their
patronage, as well as loyalty
rewards. High-risk customers
can be charged a higher
interest rate to accommodate
their higher risk, or be denied
loans entirely.
OutcomeApproach
The algorithm will look at past
repayments, credit scores,
and outstanding dues, as well
as their current economic
situation to discern which
category a customer fits into.
Not all customers are equal. Some customers have higher spending potential, making them better
safer opportunities for loans. But there are other customers who won’t be as reliable when it
comes time to collect for loan repayments.
New visitorNew visitor
Repeat
Visitor
Existing
Customer
Existing
customer
Clicked adwords
campaign
interested in auto
insurance
Has Auto Policy Has Auto Policy
Spend 5+ minutes
on side this week
Has Home
Policy
Upcoming Renewal
In Atlanta, GA
Last Visited three
months ago
upsell opportunity

6. 5
By optimizing asset lending
and loan servicing, financial
institutions can expect to
find greater revenue from
their investment and loan
portfolios.
Outcome
Transaction
Data Analytics
With transaction data
analytics, financial
institutions can maximize the
potential number of buyers
and increase purchase price
of assets the financial
institution puts out.
Business Need
Transaction analytics uses data, technology and advanced quantitative analysis to drive accurate
observations and insights. This is basically the usage of knowledge and raw data to replace
speculation.
Approach
By analyzing the data between
past transactions for assets
that are delivered to buyers
for investment and
expenditure options, we can
gain insights into what buyers
will buy, and what kind of
loans are attractive to
borrowers.

7. 6
The Process of
Analytics Adoption
Adopting analytics into finance is not a one-size-fits-all solution. What works for one bank may not
work for another. The magnitude, size, goals, and complexity of the implementation all factor into
the equation. There are some questions companies must answer before adopting analytics.
• Identifying internal use cases
• Measuring Analytics
• Finding Required Talent
• Technical requirements
Finding Required Talent Technical Requirements
Identifying Internal Use Cases measuring Analytics
Data analytics needs people with specialized
skills to implement and monitor properly.
Different retailers will have differing requirements
in finding this talent. There are some key questions to
be asked before finding the resources.
Can they afford to have an in-house team working on
analytics?
Do they need a permanent analytics team in-house all
the time?
Can they use an analytics firm to fulfil their needs?
By asking these questions, a company can understand
what kind of analytics requirement they can employ.
Before implementing analytics, it’s important to
identify a set of problems the solution will eventually
be tailored around. These problems are what will
create the base skeleton upon which the insights are
discovered, and solutions are crafted.
These use cases should also be classified according to
severity or magnitude of impact. There can be a
plethora of use cases in any retailer’s working space,
but some issues will create greater impact than
others. When working on a budget, it’s important to
ensure that companies focus on their biggest
priorities.
After these use cases are set, the next step
is to measure the data and the impact of
the future solutions.
The next step is to measure the impact of those
changes. This means asking a few key questions.
What are the performance goals after deploying the
solution? How are these goals going to be measured?
Does our organization have the tools necessary to
measure them?
This will provide valuable insight into finding out
whether or not the deployed solution is actually
performing as intended, or if it is causing any
unintended side effects.
The final thing to consider when looking
into analytics is learning what are the
technical requirements for the analytics
results you’re looking for.
Some financial institutions are focused on preventing
loan defaulting, while others are looking to broaden
their investment portfolios.
Different goals have different technical requirements,
and it’s imperative that companies looking to employ
analytics solutions have a clear idea of what they need,
what they already have on hand, and what they need to
acquire.

8. We hope this gave you better insight into how Data Analytics can help your company reach new
business goals. If you have any questions, please contact us using the details below.
Thank You
PositiveNaick Analytics Ltd. No177,1st floor,
LM Tech Park, 1st Main Rd, Nehru Nagar, Kottivakkam,
Chennai, Tamil Nadu 600041.
Email: customercare@positivenaick.com
Website: www.positivenaick.com
Phone: +91-44 4857 6162