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Banking Business and Banking Instruments

Having discussed some amount of mandatory regulatory compliances for banks over the past couple of blogs, let us now focus on the bank’s lines of business. Understanding the different banking products is inevitable for credit risk management and analytics. One has to be well versed with the nature of banking products before they step in to develop model for any of them.  Each banking product has its own characteristics and its own set of risk exposure. Hence, understanding these products is the top priority. In this blog we discuss three of the major banking products: Checking Accounts, Savings accounts and Certificate of Deposits.




Checking Accounts: This is a transactional deposit account held at a financial institution that allows for withdrawal and deposits. Money held in a checking account is liquid, and can be easily withdrawn using checks, automated cash machines, and electronic debits among other methods. It allows for numerous withdrawals, unlimited deposits etc. These accounts are known as current accounts in UK. These are often loss leaders for large commercial banks since they become highly commotized. Because money held in checking accounts is so liquid, aggregate balances nationwide are used in the calculation of M1 money supply.


Success factors for Business Intelligence program

To implement a successful Business Intelligence program, one needs to understand the dimensions that are critical to success of the BI program. Here we will discuss six critical success factors for the BI program.

Success factors for Business Intelligence program

Critical Dimension 1 – Strong Executive Support

If there is one dimension or critical attribute that has major influence on successfully implementing the BI program would be strong executive support. If there is any lack of enthusiasm at the top will filter downwards. A key component of obtaining strong executive support is a convincing and detailed business case for BI.


Facts about Remittances for Credits and Rent Losses – Part 1

Facts about Remittances for Credits and Rent Losses – Part 1


A valuation store, built up and kept up by charges against the bank’s working salary, is what we know by The Allowances for Loans and Lease Losses (ALLL). As an assessment measure, it is an evaluation of invalid sums that is utilized to decrease the book estimation of credits and rents to the sum that is relied upon to be gathered. The ALLL frames a piece of Capital of Tier-2; henceforth it is kept up to cover misfortunes that are plausible and admirable at the time of assessment. It does not work as a support against all conceivable future misfortunes; that assurance is given by the Capital of Tier 1. For building up and keeping up a satisfactory payment, a bank ought to:


Measuring Why Correlation does not Causation

How about we first examine the thought of the connection and its application in the process of data analysis? Connection analysis is being utilized to distinguish or evaluate the relationship between two quantitative variables. The existence of variables should be between either dependent or free variable. To quantify between the reaction and indicator variable ‘r’ is used, which is the Connection Coefficient. The connection coefficient’s indication shows the affiliation’s bearing. The bearing should be either positive affiliation or negative affiliation. For instance, a connection of r = 0.95 demonstrates an in number, positive relationship between the two variables. Then again, if a relationship r = – 0.3 demonstrates a powerless, negative relationship between the two variables. The size of the relationship coefficient demonstrates the affiliation’s quality. In connection analysis, we can fall upon just four situations of affiliation.


Situation 1 – The two variables have an in-number positive connection where r = 0.9

Situation 2 – The two variables have a powerless relationship where r = 0.3

Situation 3 – The two variables do not have any connection where r = 0

Situation 4 – The two variables have an in-number negative connection where r = – 0.9

Utilization of Case Correlation:

Promotion supervisor needs to distinguish the discriminating variable that is influencing the Conversion Rate of a site.

Business administrators need to discover whether the web journal redesign, identified with free arrival of online games, is creating the extra offer of income on the agreed day.

DayVisitors – Free Online Games Release UpdateRevenue

You may utilize excel function CORREL () in order to recognize the connection coefficient to quantify the relationship between the guests and the income. The relationship coefficient r for the aforementioned set of data is 0.90. It demonstrates that there is a solid relationship between the variable guests and income. Another flawless case for an in-number negative relationship is that at whatever point the precipitation diminishes, the horticulture’s yield diminishes. The relationship analysis likewise serves to further develop the analysis in multivariate insights.


Connection does not infer Causation:

This happens as soon as you attempt to discover the relationship between two autonomous variables or between an indigent variable and free variable. Association does not infer Causation implies those occasions that take place to correspond with one another – are not as a matter essentially related in a causal manner. This might be passed on that the variable X does not have an impact on the variable Y. It’s only an occurrence. We need to further accept or make it a theory that X is bringing about the impact on the Y variable. In the aforementioned utilization case, we discovered that the connection coefficient was at 0.89. It just demonstrates that there is a solid relationship between our Y Variable income and the X variable guests. Nonetheless, we don’t have any verification that if there is an expansion in the guests then the income additionally increments. No circumstances and end results is oblique here.


Big Data- Down to the Tidbits

Any data difficult to process or store on conventional systems of computational power and storage ability in real time is better known as Big Data. In our times the growth of data to be stored is exponential and so are its sources in terms of numbers.

Big Data has some other distinguishing features which are also popularly known as the six V’s of Big Data and they are in no particular order:

  • Variable: In order o illustrate the variable nature of Big Data we may illustrate the same through an analogy. A single item ordered from a restaurant may taste differently at different times. Variability of Big Data refers to the context as similar text may have different meanings depending on the context. This remains a long-standing challenge for algorithms to figure out and to differentiate between meanings according to context.
  • Volume: The volume of data as it grows exponentially in today’s times presents the biggest hurdle faced by traditional means of systems for processing as well as storage. This growth remains very high and is usually measured in petabytes or thousands of terabytes.
  • Velocity: The data generated in real time by logs, sensors is sensitive towards time and is being generated at high rates. These need to be worked upon in real time so that decisions may be made as and when necessary. In order to illustrate we may cite instances where particular credit card transactions are assessed in real time and decided accordingly. The banking industry is able to better understand consumer patterns and make safer more informed choices on transactions with the help of Big Data.

Big Data & Analytics DexLab Analytics

  • Volatile: Another factor to keep in mind while dealing with Big Data is how long the particular data remains valid and is useful enough to be stored. This is borne out by necessity of data importance. A practical example might be like a bank might feel that particular data is not useful on the credibility of a particular holder of credit cards. It is imperative that business is not lost while trying to avoid poor business propositions.
  • Variety: The variety of data makes reference to the varied sources of data and whether it is structured or not. Data might come from a variety of formats such as Videos, Images, XML files or Logs. It is difficult to analyze as well as store unstructured data in traditional systems of computing.

Most of the major organizations that are found in the various parts of the world are now on the lookout to manage, store and process their Big Data in more economical and feasible platforms so that effective analysis and decision-making may be made.

Big Data Hadoop from Apache is the current market leader and allows for a smooth transition. However with the rise of Big Data, there has been a marked increase in the demand for trained professionals in this area who have the ability to develop applications on Big Data Hadoop or create new data architectures. The distributed model of storage and processing as pursued by Hadoop gives it a greater advantage over conventional database management systems.



One fine day people realized that it is raining gems and diamonds from the sky and they start looking for a huge container to collect and store it all, but even the biggest physical container is not enough since it is raining everywhere and every time, no one can have all of it alone, so they decide to just collect it in their regular containers and then share and use it.

Since the last few years, and more with the introduction of hand-held devices, valuable data is being generated all around us. Right from health care companies, weather information of the entire world, data from GPS, telecommunication, stock exchange, financial data, data from the satellites, aircrafts to the social networking sites which are a rage these days we are almost generating 1.35 million GB of data every minute. This huge amount of valuable, variety data being generated at a very high speed is termed as “Big Data”.



This data is of interest to many companies, as it provides statistical advantage in predicting the sales, health epidemic predictions, climatic changes, economic forecasts etc. With the help of Big Data, the health care providers, are able to detect an outbreak of flu, just by number of people in the geography writing on the social media sites “not feeling well.. down with cold !”.

Big data was used to locate the missing Malaysian flight “MH370″. It was Big Data that helped analyze the million responses and the impact of the very famous TV show “Satyamev Jayate”. Big data techniques are being used in neonatal units, to analyze and record the breathing pattern and heartbeats of babies to predict infections even before the symptoms appear.

As they say, when you have a really big hammer, everything becomes a nail. There is not a single field where big data does not give you the edge, however processing of this massive amount of data is a challenge and hence the need of a framework that could store and process data in a distributed manner (the shared regular containers).

Apache Hadoop is an open source framework, developed by Doug Cutting and Mike Cafarella in 2005, written in java for distributed processing and storage of very large data sets on clusters of normal commodity hardware.

It uses data replication for reliability, high speed indexing for faster retrieval of data and is centrally managed by a search server for locating data. Hadoop has HDFS (Hadoop Distributed File System) for the storage of data and MapReduce for parallel processing of this distributed data. To top it all, it is cost effective since it uses commodity hardware only, and is scalable to the extent you require. Hadoop framework is in huge demand by all big companies. It is the handle for the Big hammer!!


BASEL and Capital Adequacy Requirements

In one of our previous blogs, we had initiated a discussion on the BASEL accords- a set of recommendations on banking regulations aimed at ensuring adequate capital for financial institutions such that they can absorb unexpected loss. Presently, we try to dig deeper into the different characteristics of the accords and their recommendations for capital adequacy.

As a regulatory capital requirement framework BASEL has evolved over time. The first set of Basel accords, BASEL-I created a risk insensitive minimum capital requirement. BASEL-II has been a huge development over its ancestor, as it had explicit emphasis on identifying different risk sources and allocating financial capital for each of them. BASEL-III is a more conservative version of BASEL-II. In this blog, we will focus on explaining minimum capital requirements prescribed in BASEL-II.

The minimum capital requirements are defined as the capital required, covering the three main areas of risk: Credit Risk, Operational Risk and Market Risk. Credit risk is the risk that arises from the default of making required payments on debt. Operational risk arises from failed internal processes (such as legal risk. Strategy and Reputation risk falls outside the purview). Market risk arises from losses on and off balance sheet position arising from movement in market prices. For the estimate of minimum capital requirements, the Risk-Weighted Assets must be calculated.

Why are Risk-Weighted Assets important in calculating minimum capital requirement?

Not all assets in a bank’s balance sheet are equally risky. For e.g. cash in an ATM is safer than a sub-prime mortgage. So regulatory capital must be set in relation to the riskiness of the asset rather than just by the value of the asset in the balance sheet. For Risk weighting asset, off-balance sheet as well as on-balance sheet items must be included. The idea is to prevent banks from creating tons of off-balance sheet assets and claiming there’s no risk at all. Off-balance sheet items include: financial instruments like forwards & future options, credit default swaps etc. Basel II prescribes the following risk weights across asset classes:

AssetsRisk Weights
Cash and Equivalents0%
Residential Mortgages35%
Credit/ auto loans75%
Commercial Real Estate100%
Govt. SecuritiesBy Rating
Interbank loans/Corporate LoansBy Rating
Other assets100%

Microsoft acquires VoloMetrix

Microsoft has acquired VoloMetrix to boost organizational analytics capabilities. This acquisition will combine VoloMetrix experience, technology and track record of success with Office 365 and Microsoft’s previously announced Delve Organizational Analytics. With this acquisition, Microsoft is aiming to fulfil its ambition to reinvent productivity and business process and how it will deliver new value to its customers with organizational analytics.
Organizational analytics helps businesses to measure performance metrics such as productivity, effectiveness and efficiency. This would lead to improve the profitability of the organization.

Unquestionably, the most important asset any company has is its people. Every day, it is people that make the decisions that effectively determine every company outcome. Historically there has been no data-driven way to connect employee behavior to business outcomes. Today, with the advent of powerful big data and predictive tools, a new field is emerging to solve this problem: people analytics. VoloMetrix is the leading people analytics company, using big data to optimize businesses’ performance by simplifying organizational structure, boosting employee engagement and increasing sales team effectiveness. Working with Fortune 100 companies worldwide, VoloMetrix’s patented technology extracts and analyzes anonymous aggregated collaboration data to reveal unprecedented insights into how employee behaviors drive business outcomes.

Ryan Fuller, Co-Founder and CEO at VoloMetrix said in his blog, He had started VoloMetrix 4.5 years ago with the belief that people are every company’s most valuable asset and the mission to transform knowledge worker productivity through data, transparency and feedback loops. His goals were to fundamentally change companies understanding of how their people drive their outcomes and empower every employee to take back their time and have the very best tools to be successful. He had opportunity to work with dozens of global 2000 companies to prove out the science of Organizational Analytics and apply it to help organization.
VoloMetrix has an excellent capability and the applications of the software for the organizations to deliver the productivity improvement metrics.
It has three high level solutions for the organizations:

1.    Sales Productivity
2.    Organizational Simplification
3.    Employee Engagement

Each solution has various sub dimension capabilities to solve the problems related to organizational analytics. They are Margin Optimization, Coordination, Predictive Analytics, Time Budgeting, Process delivery, Cost reduction, Workforce planning, Org network analysis, Collaboration. These capabilities address the area where the organization can leverage to improve the productivity.

Who needs what?

Sales Productivity – Sales Leadership, Sales Operations, CMOs and CFOs.
Organizational Simplification – CIOs, COOs, Corporate Strategy Teams and Consultants
Employee Engagement – Corporate Strategy, HR, CEOs, COOs and Strategy Consultants.
This acquisition can certainly help Microsoft to provide the best possible integration of VoloMetrix into Office 365.


Banking Business and Banking Instruments-3: Mortgages

How to Leverage AI Strategy in Business?

In this blog we discuss the final banking instrument- Mortgages, for which models are developed extensively. A mortgage is a debt instrument, secured by the collateral of specified real estate property that the borrower is obliged to pay back with a pre-determined set of payments. Mortgages are used by individuals and businesses to make large real estate purchases without paying the entire value of the purchase upfront.




Mortgages are mainly of two types: (a) Traditional Mortgages (b) Adjusted Rate Mortgages.


Traditional Mortgage is a fixed rate mortgage, where the borrower pays the same a fixed rate of interest for the life of the mortgage. The monthly principal and the interest payments never change from the first payment to the last. Most fixed rate mortgages have a 15-30 year term. If the market interest rate rises, the borrowers’ payment does not change. If the market interest rate drops significantly, the borrower may secure the lower rate by re-financing the mortgage.


Data Preparation using SAS

Data Preparation using SAS

Before doing any data analysis, there are tasks which are critical to the success of the data analysis project. That critical task is known as data preparation. You may have heard that in the last years the data production is expanding at an astonishing pace. Experts now point to a 4300% increase in annual data generation by 2020. This can be due to the switch from analog to digital technologies and the rapid increase in data generation by individuals and corporations alike. The most of the data generated in the last few years are unstructured.