Category Archive: credit risk analysis

How Credit Risk Modeling Is Used to Assess Credit Quality

Given the uproar on cyber crimes today, the issue of credit risk modeling is inevitable. Over the last few years, a wide number of globally recognized banks have initiated sophisticated systems to fabricate credit risk arising out of significant corporate details and disclosures. These adroit models are created with a sole intention to aid banks in determining, gauging, amassing and managing risk across encompassing business and product lines.

 

How Credit Risk Modeling Is Used to Assess Credit Quality

 

The more an institute’s portfolio expands better evaluation of individual credits is to be expected. Effective risk identification becomes the key factor to determine company growth. As a result, credit risk modeling backed by statistically-driven models and databases to support large volumes of data needs tends to be the need of the hour. It is defined as the analytical prudence that banks exhibit in order to assess the risk aspect of borrowers. The risk in question is dynamic, due to which the models need to assess the ability of a potential borrower if he can repay the loan along with taking a look at non-financial considerations, like environmental conditions, personality traits, management capabilities and more.

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Analyze the Risk of a Borrower with These Sure-fire Credit Risk Analytics Techniques

It’s a hard but true fact – no more do businesses survive without leverages. In a quest for success and expansion, they need to resort to debt, because equity alone fails to ensure survival. Be it funding a new project, fulfilling working capital requirement or expanding business operations, an organization needs funding for various corporate activities.

 

Analyze the Risk of a Borrower with These Sure-fire Credit Risk Analytics Techniques

 

Talking of India, the credit market scenario in here is not so matured in comparison to other developed countries; hence there exists an excessive dependency level on conventional banking structure. Nevertheless, raising finance from issuance of bonds by companies is also not so rare – majority of companies in need of capital raise money from bonds and shares and this practice is widely prevalent throughout the nation.

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Credit Risk Modelling: How Indian Fintech Startups Are Hitting a Home Run

After scoring high with top notch conglomerates, Indian economy is heating up more than ever – because of flourishing Indian fintech establishments that are popping up here and now.

 
Credit Risk Modelling: How Indian Fintech Startups Are Hitting a Home Run
 

In this blog, we will take a deeper look down into the mechanism how startups are doing well for themselves in this competitive world from a credit risk perspective. For that, we will dig deep into the personal account of an employee working in one of the notable startups in India, which deals with data analytics product for the financial services industry – what experiences he gathered while working in a startup sector, what advices he would like share and things like that will help us crack this industry better.

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Credit Risk Modelling: A Basic Overview

Credit Risk Modelling: A Basic Overview

HISTORICAL BACKGROUND

The root cause for the Financial Crisis which stormed the globe in 2008 was the Sub-prime crisis which appeared in USA during late 2006. A sub-prime lending practice started in USA during 2003-2006. During the later parts of 2003, the housing sector started expanding and housing prices also increased. It has been shown that the housing prices were growing exponentially at that time. As a result, the housing prices followed a super-exponential or hyperbolic growth path. Such super-exponential paths for asset prices are termed as ‘bubbles’ So USA was riding a Housing price bubble. Now the bankers, started giving loans to the sub-prime segments. This segment comprised of customers who hardly had the eligibility to pay back the loans. However, since the loans were backed by mortgages bankers believed that with housing price increases the they could not only recover the loans but earn profits by selling off the houses. The expectations made by the bankers that asset prices always would ride the rising curve was erroneous. Hence, when the housing prices crashed the loans were not recoverable. Many banks sold off these loans to the investment banks who converted the loans into asset based securities. These assets based securities were disbursed all over the globe by the investments banks, the largest being done by Lehmann Brothers. When the underlying assets went valueless and the investors lost their investments, many of the investment banks collapsed. This caused the Financial Crisis and a huge loss of investors and tax-payers wealth. The involvement of Systematically Important Financial Institutions (SIFIs) and Globally Systematically Important Financial Institutions (G-SIFIs) into the frivolous lending process had amplified the intensity and the exposure of the crisis.

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Banks Merged With Fintech Startups to Perform Better Digitally

Axis Bank has acquired FreeCharge, a mobile wallet company opening doors to many such deals in the future. As a consequence, do you think banks and fintech startups have started working towards a common goal?

 
Banks Merged With Fintech Startups to Perform Better Digitally
 

On some day in the early 2016, Rajiv Anand, the Executive Director of Retail Banking at Axis Bank, asked his team who were hard at work, “Do present-day customers know how a bank would look in the future?”

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Sources Of Banking Risks: Credit, Market And Operational Risks

Banking risk refers to the future uncertainty which creates stochasticity in the cash flow from receivables of outstanding balances. Banking Risks can be described in the Vonn-Neumann-Morgenstern (VNM) framework of Money lotteries. In this framework, the set of outcomes are assumed to be continuous and monetary in nature, and the lottery is a list of probabilities associated with the continuous outcomes. When applied to the banking framework, the cash flows (the set of outcomes) are assumed to be continuous and stochastic in nature. A theoretical model for the risk is represented in the framework below:

 
Sources Of Banking Risks: Credit, Market And Operational Risks
 
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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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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.

 
Regulatory Credit Risk Management: Improve Your Business with Efficient CRM
 

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.

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SAS and Equifax Clouts Deep Learning and AI to Improve Credit Risk Analysis

SAS and Equifax Clouts Deep Learning and AI to Improve Credit Risk Analysis

The noteworthy triumphs over us, humans, in Poker, GO, speech recognition, language translation, image identification and virtual assistance have enhanced the market of AI, machine learning and neural networks, triggering exponential razzmatazz of  Apple (#1 as of February 17), Google (#2), Microsoft (#3), Amazon (#5), and Facebook (#6). While these digital natives command the daily headlines, a tug of war has been boiling of late between two ace developers –  Equifax and SAS – the former is busy in developing deep learning tools to refine credit scoring, and the latter is adding new deep learning functionality to its bouquet of data mining tools and providing a deep learning API.

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Darker Clouds Covering the Cloud

Darker Clouds Covering the Cloud
 

New age technologies are dominating the present business environment. Mobility, cloud computing, social media and analytics have been affecting the different realms of business at an ever-increasing rate. Though most of the impacts are favourable, yet it will be reckless to ignore the severity of the negative ones.

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