Today, an effective dashboard is mostly insight-driven. And since a good lot of analysis projects stand upon spatial data, playing with maps is an indispensable skill you need to have in your visualization toolbox.
Here, we will like to share a few handy tips to improve the analytic and aesthetic value of maps in your dashboard:
Continue reading “5 Ways to Enhance Value of Your Dashboards Using Maps”
Banks, as financial institutions, play an important role in the economic development of a nation. The primary function of banks had been to channelize the funds appropriately and efficiently in the economy. Households deposit cash in the banks, which the latter lends out to those businesses and households who has a requirement for credit. The credit lent out to businesses is known as commercial credit(Asset Backed Loans, Cash flow Loans, Factoring Loans, Franchisee Finance, Equipment Finance) and those lent out to the households is known as retail credit(Credit Cards, Personal Loans, Vehicle Loans, Mortgages etc.). Figure1 below shows the important interlinkages between the banking sector and the different segments of the economy:
Banks borrow from the low-risk segment (Deposits from household sector) and lend to the high-risk segment (Commercial and retail credit) and the profit from lending is earned through the interest differential between the high risk and the low risk segment. For example: There are 200 customers on the books of Bank XYZ who deposit $1000 each on 1st January, 2016. These borrowers keep their deposits with the bank for 1 year and do not withdraw their money before that. The bank pays 5% interest on the deposits plus the principal to the depositors after 1 year. On the very same day, an entrepreneur comes asking for a loan of $ 200,000 for financing his business idea. The bank gives away the amount as loan to the entrepreneur at an interest rate of 15% per annum, under the agreement that he would pay back the principal plus the interest on 31st December, 2016. Therefore, as on 1st January, 2016 the balance sheet on Bank XYZ is:
Consider two scenarios:
This is a win – win situation for all. The pay-offs were as follows:
Entrepreneur: Met the capital requirements of his business through the funding he obtained from the bank.
Depositors: The depositors got back their principal, with the interest (Total amount = 1000 + 0.05 * 1000 = 1050).
Bank: The bank earned a net profit of 10%. The profit earned by the bank is the Net Interest Income = Interest received – Interest Paid (= $30,000 – $10000 = $20,000).
This is a drastic situation for the bank!!!! The disaster would spread through the following channel:
Entrepreneur: Defaults on the whole amount lent.
Bank: Does not have funds to pay back to the depositors. Hence, the bank has run into liquidity crisis and hence on the way to collapse!!!!!!
Depositors: Does not get their money back. They lose confidence on the bank.
Only way to save the scene is BAILOUT!!!!!
The Second Scenario highlighted some critical underlying assumptions in the lending process which resulted in the drastic outcomes:
Observation: The sources of borrower and transaction risks associated with an obligor must be duly assessed before lending out credit. A basic tenet of risk management is to ensure that appropriate controls are in place at the acquisition phase so that the affordability and the reliability of the borrower can be assessed appropriately. Accurate appraisal of the sources of an obligor’s origination risk helps in streamlining credit to the better class of applicants.
Observation: The bank failed to keep back sufficient reserves to fall back up on, in case of defaults. Two extreme lending possibilities for a bank are: a. Bank keeps 100% reserves and lends out 0%, b. Bank keeps 0% and lends out 100%. Under the first extreme, the bank does not grow at all. Under the second extreme (which is the case here!!!) the bank runs a risk of running into liquidation in case of a default. Every bank must solve an optimisation problem between risk and growth opportunities.
The answers to these questions to be discussed in the subsequent blogs.
Stay glued to our site for further details about banking structure and risk modelling. DexLab Analytics offers a unique module on Credit Risk Modelling Using SAS. Contact us today for more details!
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Since the start of this year, new development in the field of technology has been the hottest topic of discussion at several science symposiums. This blog post sheds some light on what can be expected for 2017, based on 2016 evolutions in Data Science and Machine Learning.
Continue reading “Top 2016 Trends Expected to Turn Fruitful in 2017”
Data Visualization can be your bow and arrow provided you know the exact way to use it.
In modern day scenario, data visualization has become the crux of efforts – raw data in various forms and statistics tends to be incredibly powerful, but only if you decide to work with them as a whole. After all, it’s not just the numbers but the story behind those numerical figures that reveals something. So, you require data visualization to brush up these notions and turn them into something more compelling to target audience. Data Visualization makes your messages more attractive, lively and enhances the impact, along with keeping your audience hooked.
Continue reading “What to Do and What Not to Do With Data Visualization”
In the aftermath of the Great Recession and the credit crunch that followed, the financial institutions across the globe are facing an increasing amount of regulatory scrutiny, and for good reasons. Regulatory efforts necessitate new, in-depth analysis, reports, templates and assessments from financial institutions in the form of call reports and loan loss summaries, all of which ensures better accountability, thus helping business initiatives.
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Also, regulators have started asking for more transparency. Their main objective is to know that a bank possesses thorough knowledge about its customers and their related credit risk. Moreover, new Basel III regulations entail an even bigger regulatory burden for the banks.
For the most effective credit risk management solution, one needs to gain in-depth understanding of a bank’s overall credit risk. View individual, customer and portfolio risk levels.
While banks give immense importance for a structured understanding of their risk profiles, a lot of information is found strewn across among various business units. For all this and more, intensive risk assessment is needed, otherwise bank can never know if capital reserves precisely reveal risks or if loan loss reserves sufficiently cover prospective short-term credit losses. Banks that are not in such good shape are mostly taken under for close scrutiny by investors and regulators, as they may lead to draining losses in the future.
Adopt a well-integrated, comprehensive credit risk solution. It helps in curbing loan losses, while ensuring capital reserves that strictly reflect the risk profile. Owing to this solution, banks buckle up and run quickly to coordinate with simple portfolio measures. Fortunately, it will also lead to a more sophisticated credit risk management solution, which will include:
In summary, if your credit risk is controlled properly, the rest of the things are taken care by themselves. To manage credit risk perfectly, rest your trust on credit risk professionals – they understand the pressing needs of decreasing default rates and improving the veracity with which credit is issued, and for that, they need to devise newer ways and start applying data analytics to Big Data.
Get more insights on credit risk management including articles, research and other hot topics, follow us at DexLab Analytics. We offer excellent credit risk management courses in Delhi. For further queries, call us today!
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Before delving deeper into the analysis of bank data using R, let’s have a quick brush-up of R skills.
As you know, R is a well-structured functional suite of software for data estimation, manipulation and graphical representation.
Continue reading “Classifying Bank Customer Data Using R? Use K-means Clustering”
Artificial Intelligence projects, applications and platforms are being churned out from every corner of the world. A majority of them now possess the ability to break loose lab life and hit mainstream trends, making an appearance in myriad online tools, open source APIs and mass gadgets.
Though the machines are yet to take over our lives, they are filtrating their way into our lives, influencing day-to-day activities, be it work or entertainment. From personal assistants like Alexa and Siri, to self-driving vehicles powered by predictive modeling and more intense and fundamental machine learning technologies, a wide set of applications of AI are in use of late.
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We perused through a handful number of AI apps so that we can enlist the ones that are more practical and thus really deserving! Let’s leverage piles of data with these effective applications:
As per Creative Strategies report, 70% of iPhone users have used Siri at least for once or sometimes, but everyone has tried it at least. We are here to tell you don’t hire a personal assistant, instead implement Siri.
This voice-powered virtual assistant makes business operations smoother and hassle-free, while making your workday more productive. The software is activated by voice, and it is at present available in 20 languages.
Developed and powered by Amazon for Amazon Echo intelligent speaker, Alexa, a robust voice service was launched in 2014. It can help you in ordering supplies, translating and controlling office’s vacuum.
However, connecting your Echo to IFTTT may allow you to coordinate with services that aren’t supported originally by the Echo, while allowing you to integrate multiple actions into a single command to the Echo.
This is one of the most popular artificial intelligence applications. Google Now functions by keeping a tab on your calendar, mail, web searches and lot more, along with sending relevant alerts and news on your device as and when detected. It can also carry out tasks, and answer queries, based on voice commands.
The best part of this application is that you don’t have to log in to use it. Just set up alerts that will be sent to the device, and that’s all. At present, it is available in English and is considered a tailing rival of Siri.
If you know the exact way to maneuver it, Cortana would be the most effective AI personal assistant. It can perform all sorts of things, right from dictating and sending emails, tracking flights to searching something on the internet or checking weather forecasts. The more time you spent on it, its functionality gets better and better.
Even, the company is so impressed by its services that it has integrated the service into Power BI, its most intuitive BI tool.
Brain Artificial, aka Braina is self-regulating software, which enables easy hands-free operation in your computer to perform basic tasks by listening to voice based commands in English language.
Braina enjoys a certain edger over its run of the mill competitors as it can precisely work with a variety of accents, which is not so common. The pro version is equipped with a bonus of deep learning – it is programmable as well as observes user behavior over time.
Hope, AI applications serves the humanity well!
Check out some more interesting stuff on Machine Learning at DexLab Analytics. We offer world-class machine learning courses in Delhi for all your data aspirations. Come, explore!
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Machines are going to eat our jobs.
AI is handling insurance claims and basic bookkeeping, maintaining investment portfolios, doing preliminary HR tasks, and performing extensive legal research and lot more. So, do humans stand a chance against the automation apocalypse, where everything, almost everything will be controlled by robots?
What do you think? You might be worried about your future job opportunities and universal basic income, but I would ask you to draw a clearer picture about this competing theory – because, in the end, this question might not even be a plausible and completely valid question. Why, I will tell you now.
Continue reading “Automation Doesn’t Necessarily Make Humans Obsolete, Here’s Why”
Big Data is the latest buzz. It has to be effectively analyzed to formulate brilliant marketing and sales strategies. It’s of immense importance, as it includes humongous amount of information accumulated about customers from numerous sources like email marketing schemes and web analytics.
However, due to the vast magnitude of information available, it may get quite difficult for marketers to analyze and evaluate all the data in an efficient way. Fortunately, plenty of tools are available in the market that can manage mammoth marketing data and here are few of them:
Continue reading “Top 10 Nifty Tools to Manage Big Marketing Data for Companies”