online courses Archives - Page 4 of 16 - DexLab Analytics | Credit Risk | Market Risk | SAS Python Machine Learning Modeling

Tableau: The intelligent tool of business

Tableau: The intelligent tool of business.
 

Tableau plays a very crucial role in data intelligence, data representations, data visualizations, and data modelling. The time has gone when folks used to draw graph, charts, on excel and variant types of resources or tool. Because provide us live data source accessibility with number of data sources like – spark, Google Products, Social network, programming language (R,Python,JAVA) whereas tableau uses VIZQL in background to speed up the performance. So now the time is to move towards Business Intelligence.As of now, you don’t need to worry about either data or data sources.

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Here’s why SAS Analytics Is a Must-Have IT Skill to Possess

Here’s why SAS Analytics Is a Must-Have IT Skill to Possess

Without the great Analytical surge, everything was looking fit and fine. The economy was performing well. The IT industry was looking stable. The tech honchos were playing fine. And then IT happened! Data Analytics snatched the dazzling limelight all to itself.

It’s true once in a while, our market needs a good shaking, or else things tend to get sluggish and slow. Over time, the industries start decreasing in efficiency and business houses crumples. Therefore, the change induced by Big Data Analytics is one for good: it started pulling back the market to its former position. From medical science to military to security, the reach of Big Data Analytics can be witnessed everywhere.

The evolution of analytics is largely consistent and covers a wide span of industries. It’s not like it suddenly came into a lot of focus, its advancement was slow and steady. Now, it has strived to become extremely important to store, interpret, analyze and develop crucial insights – social media is deriving maximum benefits out of analytics, while customizing their products to make more money from advertisements. On the other hand, the service-oriented companies love to manipulate data that is generated through myriad social channels to trigger customer base.

The ABC of Summary Statistics and T Tests in SAS – @Dexlabanalytics.

Today, SAS certifications are extremely rewarding and scores high for both employee and employer. Analytics is a big word, encompassing a whole array of job roles, such as Forecaster, Market Researcher, Data Miner, Operations Researcher and Statistical Analyst – so when are you choosing this career gateway for a better future! DexLab Analytics is here with its state-of-the-art SAS training courses, help yourself.

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3 key benefits of becoming SAS analytics professional:

Increase marketability and reach

SAS Analytics professionals possess higher marketability skills and enjoy a certain edge over competitors. Their job is to deliver nothing but the best, and they are very focused in doing that, leaving no scope for complaints.

Expand credibility for being the right technical professionals

As the SAS certified professionals have a thorough know-how about using SAS Software the employers stay relaxed and trusts their predicaments, hence, enhancing their credibility quotient.

Enhance skill and expertise in SAS area of specialization

No doubt, SAS Analytics professionals are extremely good in their field of work. Owing to their professional nature they tend to attract more lucrative job opportunities.

Data Preparation using SAS – @Dexlabanalytics.

Apart from SAS, R programming is rapidly gaining popularity. Small and large companies have realized the growing the importance of these two tools. SAS combined with R language training in Delhi opens a whole gamut of striking opportunities. Having said that, companies that have stayed traditional, through its very core, have now embraced SAS and R skills, and for the right reasons.

At DexLab Analytics, we increasingly focus on making students totally data-ready. Opt for R programming certification, and give new data-hungry souls the drive to enter the world of analytics. After all, to excel in the analytics career and sail high you need to be well-equipped with SAS and R – they are the tools of combat for the future IT domain.!

 

Interested in a career in Data Analyst?

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Facebook and Microsoft Introduces ONNX: A New Open Ecosystem to Boost AI Innovation

It’s time to move beyond Artificial Intelligence frameworks. Recently, a joined effort from the Digital giants Microsoft and Facebook has paved the pathway for developers to move beyond traditional AI frameworks. The Open Neural Network Exchange (ONNX) format announced the other day that Facebook and Microsoft are on a lookout to boost AI interoperability and innovation. This piece of information was published in their own blog posts, and from there it got viral.

 
Facebook and Microsoft Introduces ONNX: A New Open Ecosystem to Boost AI Innovation
 

In Facebook’s blog post, the Social Media behemoth clearly defined its new effort is “toward an open ecosystem where AI developers can easily move between state-of-the-art tools and choose the combination that is best for them.”

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5 Ways to Enhance Value of Your Dashboards Using Maps

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.  

 
5 Ways to Enhance Value of Your Dashboards Using Maps
 

Here, we will like to share a few handy tips to improve the analytic and aesthetic value of maps in your dashboard:

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The Basics Of The Banking Business And Lending Risks:

The Basics Of The Banking Business And Lending Risks:

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:

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Figure 1: Inter Linkages of the Banking Sector with other sectors 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:

dexlab-01

Consider two scenarios:

Scenario 1: The Entrepreneur pays off the Principal plus the interest to the bank on 31st December, 2016

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).

Credit Risk Analytics and Regulatory Compliance – An Overview – @Dexlabanalytics.

Scenario2: The Entrepreneur defaults on the loan commitment on 31st December, 2016

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!!!!!

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The Second Scenario highlighted some critical underlying assumptions in the lending process which resulted in the drastic outcomes:

Assumption1: The Entrepreneur (Obligor) was assumed to be a ‘Good’ borrower. No specific screening procedure was used to identify the affordability of the obligor for the loan.

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.

Assumption2: The entire amount of the deposit was lent out. The bank was over optimistic of the growth opportunities. Under estimation of the risk and over emphasis on growth objectives led to the liquidation of the bank.

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 discussion above highlights some important questions on lending and its associated risks:

 

  1. What are the different types of risks associated with the lending process of a bank?
  2. How can the risk from lending to different types of customers be identified?
  3. How can the adequate amount of capital to be reserved by banks be identified?

 

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!

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
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To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

Top 2016 Trends Expected to Turn Fruitful in 2017

Top 2016 Trends Expected to Turn Fruitful in 2017

 

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.

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What to Do and What Not to Do With Data Visualization

What to Do and What Not to Do With Data Visualization

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.

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Regulatory Credit Risk Management: Improve Your Business with Efficient CRM

Regulatory Credit Risk Management: Improve Your Business with Efficient CRM

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.

Help yourself with credit risk analysis course online at DexLab Analytics.

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.

What are the challenges faced by CRM Managers?

  • Sloppy data management – Unable to access the data when it’s needed the most, due to inefficient data management issues.
  • No group-wide risk modeling framework – Banks need strong, meaningful risk measures to get a larger picture of the problem. Without these frameworks, it becomes really difficult to get to the tip of the problem.
  • Too much duplication of effort – As analysts cannot alter model parameters they face too much duplication of work, which results in constant rework. This may negatively affect a bank’s efficiency ratio.
  • Inefficient risk toolsBanks need to have a potent risk solution, otherwise how can they identify portfolio concentrations or re-grade portfolios to mitigate upcoming risks!
  • Long, unwieldy reporting processManual spreadsheet based reporting is simply horrible, overburdening the IT analysts and researchers.

What are the Best Practices to fight the Challenges Noted Above?

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.

Data Science Machine Learning Certification

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:

  • Improved model management, stretching over the whole modeling life cycle
  • Real-time scoring and limits monitoring
  • Powerful stress-testing capabilities
  • Data visualization capabilities and robust BI tools that helps in transporting crucial information to anyone who needs them

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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Classifying Bank Customer Data Using R? Use K-means Clustering

Before delving deeper into the analysis of bank data using R, let’s have a quick brush-up of R skills.

 

Classifying Bank Customer Data Using R? Use K-means Clustering

 

As you know, R is a well-structured functional suite of software for data estimation, manipulation and graphical representation.

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