Category Archive: Credit Risk

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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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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ANZ uses R programming for Credit Risk Analysis

At the previous month’s “R user group meeting in Melbourne”, they had a theme going; which was “Experiences with using SAS and R in insurance and banking”. In that convention, Hong Ooi from ANZ (Australia and New Zealand Banking Group) spoke on the “experiences in credit risk analysis with R”. He gave a presentation, which has a great story told through slides about implementing R programming for fiscal analyses at a few major banks.

 
ANZ uses R programming for Credit Risk Analysis
 

In the slides he made, one can see the following:

 

How R is used to fit models for mortgage loss at ANZ

A customized model is made to assess the probability of default for individual’s loans with a heavy tailed T distribution for volatility.

One slide goes on to display how the standard lm function for regression is adapted for a non-Gaussian error distribution — one of the many benefits of having the source code available in R.

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Apple Watch’s Strategy Analytics, Return to 1% Growth

As per the latest research from strategy analytics, the global smart watch shipments of Apple has grown by 1 percent annually to hit the major record of 8.2 million units in the 4th quarter of the year 2016. The growth of apple watch drove and got dominated with 63 percent in global smart watch share of market and Samsung still continues to hold its second position.
 
Apple Watch’s Strategy Analytics, Return to 1% Growth

 

Neil Mawston, the Executive Director at Strategy Analytics stated on the issue by saying – the global shipments have grown by 1 percent annually from the pre-existing 8.1 million units in quarter 4 in 2015 to 8.2 million in quarter 4 in 2016. The market shows a marked growth in the fourth quarter for growth in smart watches industry after the past two consecutive quarters for declining volumes. The smart watch growth is also seen to be recovering ever so slightly due to new product launches from other company giants. Moreover, there is a seasonal demand for these gadgets, and a giant such as Apple is launching stringer demand in the major developed markets in the US and UK. Hence, the international smart watch shipments grew by 1 percent annually; from the previously existing 20.8 million in full-year 2015 to a record high of 21.1 million in 2016.

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The Opportunities and Challenges in Credit Scoring with Big Data

Within the past few decades, the banking institutions have collected plenty of data in order to describe the default behaviour of their clientele. Good examples of them are historical data about a person’s date of birth, their income, gender, status of employment etc. the whole of this data has all been nicely stored into several huge databases or data warehouses (for e.g. relational).

 
The Opportunities and Challenges in Credit Scoring with Big Data
 

And on top of all this, the banks have accumulated several business experiences about their crediting products. For instance, a lot of credit experts have done a pretty swell job at discriminating between low risk and high risk mortgages with the use of their business mortgages, thereby making use of their business expertise only. It is now the goal of all credit scoring to conduct a detailed analysis of both the sources of data into a more detailed perspective with then come up with a statistically based decision model, which allows to score future credit applications and then ultimately make a decision about which ones to accept and which to reject.

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The Olympics Turn To Data Analysis: Canadian Olympic Committee Deals In With Analytics

The Canadian Olympic company has recently teamed up with a major Big Data Company to ramp up the analytics for the benefit of the athletes.

 
The Olympics Turn To Data Analysis: Canadian Olympic Committee Deals In With Analytics
 

Recently the COC made an announcement about an eight-year, cash and services sponsorship deal with SAS, which is an analytics software with a brag-worthy client list from varied industries, like universities, hotels, banks casinos and much more.

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Understanding Credit Risk Management With Modelling and Validation

The term credit risk encompasses all types of default risks that are associated with different financial instruments such as – (like for example, a debtor has not met his or her legal duties according to the debt contract), migrating risk (arises from adverse movements internally or externally with the ratings) and country risks (the debtor cannot pay as per the duties because of measure or events taken by political or monetary agencies of the country itself).

 

Understanding credit risk management with modelling and validation

                        Understanding Credit Risk Management With Modelling and Validation

 

In compliance to Basel Regulations, most banks choose to develop their own credit risk measuring parameters: Probability Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). Several MNCs have gathered solid experience by developing models for the Internal Ratings Based Approach (IRBA) for different clients.

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Introduction To Credit Score Cards: Its Use in Crisis

The incident we are about to describe took place during 2009 circa at a party, a year in which the world was going through one of its worst financial crisis for the longest time. Every average bloke on the streets was aware of terms like mortgage-backed securities (MBS), sub-prime lending and credit crisis, after all these are the reasons for his plight.

 

Introduction To Credit Score Cards: Its Use in Crisis

 

But at this party we are speaking of, I was fortunate enough to meet with an informed and highly compassionate elderly woman, and after a few minutes of discussion the topic came to what we here do for a living. She wanted to know more about credit scorecard systems. As I further went on to explain the details of how this system works, her expression changed from being just plainly curious to angry to pained.

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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.

 

BANKING BUSINESS AND BANKING INSTRUMENTS- Part 1

 

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.

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