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Shilpa Kadam

Sep 8, 2015

Business Analytics in Finance domainBFSI and various functions

Sr. Data Scientist

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Contents

• Types of financial institutions and their roles• Various functions of an organization• Business analytics in financial services• Banking• Insurance• Services

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Financial institutes

• A financial institution is an establishment that conducts financial transactions such as investments, loans and deposits

• Banking Financial Services and Insurance is an industry term of art for companies that provide range of services. – Banking may include core banking, retail, private,

corporate, investment, cards, etc.– Financial services may include stock-broking,

payment gateways, mutual funds, etc.– Insurance covers both life and general insurances.

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BFSI

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For example

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Some of the services in each

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Various functions of an organization

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Analytics in Financial services

• Financial institutions need to support business activities and decision making in a fashion that is timely, relevant, verifiable, and personalized to meet a variety of stakeholder requirements. 

• Financial services companies seek in-depth insights to solve critical business issues, reduce risk, and drive growth.

• By applying advanced analytics to capture and understand their data, companies in the banking and securities, insurance, and investment sectors can leverage their data to build stronger, more robust business models. In doing so, they can make more proactive decisions that deliver customer value.

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Analytics in Financial services• Data is becoming the source of significant competitive advantage

for any organization. • However, the firms are overwhelmed by the volume and complexity

of unstructured data.• Predictive analytics projects should be viewed as the combination of

people, processes and technology.• Predictive analytics have applicability to a wide range of business

processes. – In one example, an asset management firm used predictive

analytics to improve marketing efforts. – The company wanted to change from a mass marketing

approach where every prospect received the same offer to an approach that enabled personalization.

– The company used predictive analytics to predict the likelihood of a prospective customer accepting an offer.

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Common applications of predictive analytics

Fraud detection and security – Predictive analytics can help stop losses due to fraudulent activity before they occur. By combining multiple detection methods – business rules, anomaly detection, link analytics, etc. – you get greater accuracy and better predictive performance.

And in today’s world, cybersecurity is a growing concern. High-performance behavioural analytics examines all actions on a

network in real time to spot abnormalities that may indicate occupational fraud, zero-day vulnerabilities and advanced persistent threats.

Marketing –  Most modern organizations use predictive analytics to determine customer responses or purchases, as well as promote cross-sell opportunities.

Predictive models help businesses attract, retain and grow the most profitable customers and maximize their marketing spending.

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Common applications of predictive analytics

Operations – Many companies use predictive models to forecast inventory and manage factory resources. Airlines use predictive analytics to decide how many tickets to sell at each price for a flight. Hotels try to predict the number of guests they can expect on any given night to adjust prices to maximize occupancy and increase revenue. Predictive analytics enables organizations to function more efficiently. 

Risk – One of the most well-known examples of predictive analytics is credit scoring. Credit scores are used ubiquitously to assess a buyer’s likelihood of default for purchases ranging from homes to cars to insurance. A credit score is a number generated by a predictive model that incorporates all of the data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims and collections.

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Analytical life cycle

The analytical life cycle guides you through the steps needed to produce fact-based insights that ultimately lead to competitive advantage.

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Analytical life cycle• Identify the problem. Business units specify the need, scope,

market conditions and goal related to the business question they want to solve, which will lead to the selection of one or more modelling techniques.

• Prepare data for analysis. Depending on the business question and proposed analysis methods, this step involves using specialized techniques to locate, access, clean and prepare the data for optimal results. In our multifaceted data world, that could mean data from transactional systems, unstructured text files and data warehouses.

• Explore data. Now it’s time to explore the data in an interactive and visual fashion to quickly identify relevant variables, trends and relationships. (The shape of the data when variables are plotted out is called distribution of data. You can use shapes to identify the patterns.)

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Analytical life cycle• Transform data and create models. A skilled analyst or modeler builds

the model using statistical, data mining or text mining software, including the critical capability of transforming and selecting key variables. Models need to be built rapidly so modelers can use trial and error to find the model that produces the best results.

• Test and validate models. Once built, the model is registered, tested (or validated), approved and declared ready for use against your data. With a centralized model repository, you can store extensive documentation about the model, scoring code and associated metadata (data about the data) for collaborative sharing and version control necessary for auditing purposes.

• Deploy models. When approved for production use, the model is applied to new data to generate predictive insights.

• Monitor and assess models. The predictive performance of the model is monitored to ensure it is up to date and delivering valid results. If the model performance degrades, it’s time to make changes. When it no longer works or serves a business need, it is retired.

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Various functions in banking

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Analytics in banking

• Banks come across several challenges in various functions such as:– Risk and regulatory compliance, – Quality management,– Client centricity,– Track and monitor sales, margins and operational performance,– Business process improvements,– expansion into new markets, – a renewed focus on customer profitability, etc.

To address any of the above challenges banks have taken initiatives to consider what today’s analytics capabilities can offer.

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Some of the questions that can be addressed using business analytics in banking

• What is the impact of the change in laws and regulations in banking? And how does it impact the profitability?

• Who are my high-value customers?• What is the optimal risk given the scenario?• Identify any suspicious/fraudulent activities• Identify target products and services to prospects or

customers • Product pricing to increase profitability

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Approach to adopt analytics

• Prioritize the areas of focus.• Streamline the data/data management

methods.• Integrate with/build decision management

systems.• Build analytics team.• Create smart tools and techniques to

address the issues.• Connect with leadership of various

functions within the organization to communicate the insights and support them in decision making.

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Example business case: Anti-money laundering

• What is money laundering?– Money laundering is a way to conceal illegally obtained funds.

Money laundering works by transferring money in elaborate and complicated financial transactions which mislead anyone who may seek to trace and review the transactions

Many financial institutions regularly evaluate the effectiveness of their interdiction systems with an eye toward improving the quality of the monitoring, reporting and investigation process.

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Evaluating the vendors

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References• Read about SAS anti-money laundering alert systems and its working.• https://www.youtube.com/watch?v=3zFLAC89s0o• https://www.youtube.com/watch?v=z4D7f0hBolA• https://www.youtube.com/watch?v=IXBFxIXSRnE• https://www.youtube.com/watch?v=XCR_LBzur-k• http://www.ipcsit.com/vol2/94-C140.pdf

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Analytics in Insurance

• Insurance companies globally are faced with new challenges such as unforeseeable disasters, greater customer expectations, soft markets, new approaches to distribution, regulatory compliance and consolidation.

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Various operations of insurance company

• Ratemaking• Underwriting• Sales and marketing • Claim settlement• Reinsurance• Legal services• Loss control• Accounting• Information systems, etc.

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Example business case: claims management

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Analysis output: Dashboard

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Vendors in claims systems

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Research report: ROI on business analytics solutions

IDC research shows that:

• The return on investment (ROI) of business analytics solutions that incorporate

predictive analytics is about 250%, significantly higher than the 89% ROI of projects

focused only on information access and internal productivity gains.

• Benefits of predictive analytics projects are sustained over long periods of time, and

those that rely more on analytics tend to be more competitive.

• Predictive analytics projects result in many intangible or difficult to quantify benefits

that give further impetus to investment in these solutions.

• Investment in predictive analytics continues at a healthy pace, even in tough

economic times. For example, over the past 10 years, the compound annual growth

rate (CAGR) of the IDC-tracked $1.6 billion worldwide advanced analytics software

market has been 7%, compared with a 3% CAGR for the overall IT market during the

same period.

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References

• http://www.slideshare.net/maryasholevar/chapter-4-40596655

• http://www.slideshare.net/arhirst/business-analytics-solutions-from-sap-for-the-insurance-industry-july-2011

• http://www.slideshare.net/mktghexaware/hexaware-insurance-analytics-8005775

• http://www.slideshare.net/davidpittman1/data-analytics-and-the-insurance-industry?related=1

• http://www.besmart.company/MKT/Promos/2012/0612_PA/0612_businessvalue_PA.pdf

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Risk management models in financial services

• Market risk: is the risk that the value of an investment will decrease due to moves in market factors. Volatility frequently refers to the standard deviation of the change in value of a financial instrument with a specific time horizon – Value a full range of market instruments, perform stress tests

and optimize portfolios across the entire firm, and gain an enterprise view of market risk.

– Managing market risk: Today and tomorrow - McKinsey ...

VaR: The most popular and traditional measure of risk is volatility

– For investors, risk is about the odds of losing money, and VAR is based on that common-sense fact. By assuming investors care about the odds of a really big loss, VAR answers the question, "What is my worst-case scenario?" or "How much could I lose in a really bad month?"

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Value at risk

• A VAR statistic has three components: a time period, a confidence level and a loss amount (or loss percentage). Keep these three parts in mind as we give some examples of variations of the question that VAR answers:

• What is the most I can - with a 95% or 99% level of confidence - expect to lose in dollars over the next month?

• What is the maximum percentage I can - with 95% or 99% confidence - expect to lose over the next year?

There are three methods of calculating VAR: the historical method, the variance-covariance method and the Monte Carlo simulation.

Read more: An Introduction To Value at Risk (VAR) http://www.investopedia.com/articles/04/092904.asp#ixzz3l6Ekqkjk 

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Historical method

• The historical method simply re-organizes actual historical returns, putting them in order from worst to best. It then assumes that history will repeat itself, from a risk perspective. Notice the red bars that compose the

"left tail" of the histogram. These are the lowest 5% of daily returns (since the returns are ordered from left to right, the worst are always the "left tail").

The red bars run from daily losses of 4% to 8%. Because these are the worst 5% of all daily returns, we can say with 95% confidence that the worst daily loss will not exceed 4%.

Put another way, we expect with 95% confidence that our gain will exceed -4%.

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Variance-covariance method

• This method assumes that stock returns are normally distributed. In other words, it requires that we estimate only two factors - an expected (or average) return and a standard deviation - which allow us to plot a normal distribution curve

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Monte Carlo Simulation

• The third method involves developing a model for

future stock price returns and running multiple

hypothetical trials through the model.

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Risk management models in financial services

• Credit risk: is the risk of default on a debt that may arise from a borrower failing to make required payments. In the first resort, the risk is that of the lender and includes lost principal and interest, disruption to cash flows, and increased collection costs..

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Credit risk modeling

http://www.slideshare.net/MagnifyAnalyticSolutions/m-sug-draftmasite

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Vendors in credit risk modeling

http://www.celent.com/reports/beyond-basel-ii-evaluating-financial-and-credit-risk-solution-vendors-2008

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In general for BI and analytics

http://www.informationweek.com/big-data/big-data-analytics/gartner-bi-magic-quadrant-winners-and-losers/a/d-id/1114013

https://biwizard.wordpress.com/2015/02/24/gartner-2015-magic-quadrant/

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Risk management models in financial services

• Liquidity risk: is the risk that a given security or asset cannot be traded quickly enough in the market to prevent a loss (or make the required profit).

• Operational risk: is defined as the risk of loss resulting from inadequate or failed processes, people and systems or from external events.

https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1008.pdf?56d2038eb68e90a4fd0bfa9098f5b272http://www.slideshare.net/arunavnayak75/an-overview-of-the-basel-normshttps://www.math.nyu.edu/faculty/avellane/ICBI_20131120.pdf

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Vendors in the Operational risk management tools

http://www.healthit.myindustrytracker.com/en/article/79524/gartner-positioned-sas-as-a-leader-in-magic-quadrant-for-operational-risk-mana

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Summary• Financial institutes• Banking

– Anti-money laundering• Insurance

– claims management• Financial services

– credit scoring

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Exercise • Please download the data from the following link:https://archive.ics.uci.edu/ml/datasets/Bank+Marketing• Understand the business need• Find data summary• Understand the attributes. • Independent and dependent attributes• Identify any preprocessing steps that are required. State for each attribute.• Find all relationships between the attributes and provide visuals.• Which error metric do you think is suitable to evaluate the model?• Name the models that could be used for prediction• What is your model evaluation strategy?• Report the model output and error metric.• Design a dashboard to present the analysis such that it is easy for the end

users to take business decisions.

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