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DexLab Analytics is Heading a Training Session on CRM Using SAS for Wells Fargo & Company, US

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We are happy to announce that we have struck gold! Oops, not gold literally, but we are conducting an exhaustive 3-month long training program for the skilled professionals from Wells Fargo & Company, US. It’s a huge opportunity for us, as they have chosen us, out of our tailing contemporaries and hope we do fulfill their expectations!

Wells Fargo & Company is a top notch US MNC in the field of financial service providers. Though headquartered in San Francisco, California and they have several branches throughout the country and abroad. They even have subsidiaries in India, which are functioning well alike. Currently, it is the second-largest bank in home mortgage servicing, deposits and debit cards in the US mainland. Their skilled professionals are adept enough to address complicated finance-induced issues, but they need to be well-trained on tackling Credit Risk Management challenges, as CRM is now the need of the hour.

Our consultants are focused on imparting much in-demand skills on Credit Risk Modeling using SAS to the professionals for the next three months. The total course duration is of 96 hours and the sessions are being conducted online.

 

 

 

 

In this context, the CEO of DexLab Analytics said, “This training session is another milestone for us. At DexLab Analytics, being associated with such a global brand name, Wells Fargo is a matter of great honor and pride, which I share with all my team members. Thanks to their hard work and dedication, we today possess the ability and opportunity to conduct exhaustive training program on Credit Risk Management using SAS for the consultants working at Wells Fargo & Company.”

“The training session starts from today, and will last for three-months. The total session will span over 96 hours. Reinforcing our competitive advantage in the process of development and condoning data analytics skills amongst the data-friendly communities across the globe, we are conducting the entire 3-month session online,” he further added.

Credit Risk Management is crucial to survive in this competitive world. Businesses seek this comprehensive tool to measure risk and formulate the best strategy to be executed in future. Under the umbrella term CRM, Credit Risk Modeling is a robust framework suitable to measure risk associated with traditional crediting products, like credit score, financial letters of credit and etc. Excessive numbers of bad loans are plaguing the economy far and large, and in such situations, Credit Risk Modelling using SAS is the most coveted financial tool to possess to survive in these competitive times.

In the wake of this, DexLab Analytics is all geared up to train the Wells Fargo professionals in the in-demand skill of CRM using SAS to better manage financial and risk related challenges.

To read our Press Release, click:

DexLab Analytics is organizing a Training Program on CRM Using SAS for Wells Fargo Professionals

 

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How Fintechs Help Optimize the Operation of Banking Sector

How Fintechs Help Optimize the Operation of the Banking Sector

Financial technology- Fintech plays a key role in the rapidly evolving payment scenario. Fintech companies provide improved solutions that affect consumer behavior and facilitate widespread change in the banking sector. Changes in data management pertaining to the payment industry is occurring at a fast pace. Cloud-based solution and API technology (Application Programming Interfaces) has played a major role in boosting the start-up sector of online payment providers like PayPal and Stripe. As cited in a recent PwC report over 95% of traditional bankers are exploring different kinds of partnerships with Fintechs.

 Interpreting consumers’ spending behavior has enhanced payment and data security. Credit risk modeling help card providers identify fraudulent activities. The validity of a transaction can be checked using the GPS system in mobile phones. McKinsey, the consulting firm has identified that the banking sector can benefit the most from the better use of consumer and market data.  Technological advancements have led to the ease of analyzing vast data sets to uncover hidden patterns and trends. This smart data management system helps banks create more efficient and client-centric solutions. This will help banks to optimize their internal system and add value to their business relationship with customers.

Role of Big Data

 Over the past two years, the digital revolution has created more data than in the previous history of humankind. This data has wide-ranging applications such as the banks opening their credit lines to individuals and institutions with lesser-known credit-score and financial history. It provides insurance and healthcare services to the poor. It also forms the backbone of the budding P2P lending industry which is expected to grow at a compound annual growth rate (CAGR) of 48% year-on-year between 2016 and 2024.

The government has channelized the power of digital technologies like big data, cloud computing and advanced analytics to counter frauds and the nuisance of black money. Digital technologies also improve tax administration. Government’s analysis of GST data states that as on December 2017, there were 9.8 million unique GST registrations which are more than the total Indirect Tax registrations under the old system. In future big data will help in promoting financial inclusion which forms the rationale of the digital-first economy drive.

Small is becoming Conventional

Fintechs apart from simplifying daily banking also aids in the financial empowerment of new strata and players. Domains like cyber security, work flow management and smart contracts are gaining momentum across multiple sectors owing to the Fintech revolution. For example workflow management solution for MSMEs (small and medium enterprises) is empowering the industry which contributes to 30% of the country’s GDP. It also helps in the management of business-critical variables such as working capital, payrolls and vendor payments. Fintechs through their foreign exchange and trade solutions minimizes the time taken for banks to processing letter of credit (LC) for exporters. Similarly digitizing trade documents and regulatory agreements is crucial to find a permanent solution for the straggling export sector.

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Regulators become Innovators

According to the ‘laissez-faire’ theory in economics, the markets which are the least regulated are in fact the best-regulated. This is owing to the fact that regulations are considered as factors hindering innovations. This in turn leads to inefficient allocation of resources and chokes market-driven growth. But considering India’s evolving financial landscape this adage is fast losing its relevance. This is because regulators are themselves becoming innovators.

The Government of India has taken multiple steps to keep up with the global trend of innovation-driven business ecosystem. Some state-sponsored initiatives to fuel the innovative mindset of today’s generation are Startup India with an initial corpus of Rs 10,000 crore, Smart India Hackathon for crowd-sourcing ideas of specific problem statements, DRDO Cyber Challenge and India Innovation growth Program. This is what enabled the Indian government to declare that ‘young Indians will not be job seekers but job creators’ at the prestigious World Economic Forum (WEF).

From monitoring policies and promoting the ease of business, the role of the government in disruptive innovations has undergone a sea change. The new ecosystem which is fostering innovations is bound to see a plethora of innovations seizing the marketplace in the future. Following are two such steps:

  • IndiaStack is a set of application programming interface (APIs) developed around India’s unique identity project, Aadhaar. It allows governments, businesses, start-ups and developers to utilize a unique digital infrastructure to solve the nation’s problems pertaining to services that are paperless, presence-less and cashless.
  • NITI Ayog, the government’s think tank is developing Indiachain, the country’s largest block chain. Its vision is to reduce frauds, speed up enforcement of contracts, increase transparency of transactions and boost the agricultural economy of the country. There are plans to link Indiachain to IndiaStack and other digital identification databases.

As these initiatives start to unfold, India’s digital-first economy dream will soon be realized.

Advances in technologies like Retail Analytics and Credit Risk Modeling will take the guesswork and habit out of financial decisions. ‘’Learning’’ apps will not only learn the habit of users but will also engage users to improve their spending and saving decisions.

To know more about risk modeling follow Dexlab Analytics and take a look at their credit risk analytics and modeling training course.

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Breaking the Misconceptions: 4 Myths Regarding Data-Driven Financial Marketing

A majority of low-mid financial services companies toil under the wrong notion that owing to their capacity, size and scope, the complex data-driven marketing tactics are simply out of their reach – this is not true and frankly speaking quite a shame to consider even.

BREAKING THE MISCONCEPTIONS: 4 MYTHS REGARDING DATA-DRIVEN FINANCIAL MARKETING

Over the past decade, the whole concept of data analytics has undergone a massive transformation – the reason being an extensive democratization of marketing tactics. Today’s mid-size financial service providers can easily implement marketing initiatives used by dominant players without any glitch.

Besides, there are several other misconceptions regarding data and its effect on financial marketing that we hear so often and few of them are as follows:

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Myth1 – Legally, banks are only allowed to run broad-based advertising

While it’s partially true that there are certain restrictions on banking institutions when it comes to target consumers, based on income, age, ethnicity and other factors, marketers can still practice an array of tactics, both online and offline.

Marketers can leverage a pool of data for online and offline marketing to formulate data models, keeping in mind the existing customers need and preferences. Once you have an understanding of their online behavior, how they use the data power to carry out transactions, these insights can be applied to attract new customers, who exhibit similar behaviors.

Myth 2 – Data-driven marketing doesn’t bolster customer relationship

It’s a fact, Millennials, especially wants to be aware about financial services and its associated products, and are keen to understand how can banks lend an additional support to their living and social life. Companies can start building relationship based out of it, while implementing data-driven marketing perspective into them.

Myth 3 – You need a huge budget and an encompassing database to drive marketing campaigns

Corporate honchos and digital natives certainly maintain sprawling in-house database to boost marketing activities, but don’t be under the impression that mid-size institutions cannot leverage much from virtual datamart. The impressive SaaS-based solutions houses first-party data, safely and securely and offer you mechanisms that let you integrate with other third-party data, both online and offline.

Datamarts let mid-size marketers achieve a lot of crucial task success. Firstly, you will be able to link online user IDs with offline data – this lets you derive insights about your current customers, including their intents, interests and other details. The most important thing is that it will usher you to build customer models that could target newer customers for your bank.

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Myth 4 – Data-driven marketing is too much time-consuming

A lot of conventional marketers are of the opinion data-driven marketing is a huge concept – time-consuming and labor-intensive. But, that’s nothing but a myth. Hundreds and thousands of mid-size companies develop models, formulate offers and execute campaigns within a 30-day window using a cool datamart.

However, the design and execution part of campaigns need no time, whereas the learning part needs some time. You need to learn how to develop such intricate models, and that’s where time is involved.

To ace on financial models, get hands-on training from credit risk analysis course onlineDexLab Analytics offers superior credit risk management courses, along with data analytics, data science, python and R-Programming courses.

In the end, all that matters is prudent marketing campaigns powered by data yields better results than holding onto these misconceptions. So, break the shackles and embrace the power of data analytics.

The article has been sourced from – http://dataconomy.com/2017/08/5-misconceptions-data-driven-marketing

 

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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.

Continue reading “How Credit Risk Modeling Is Used to Assess Credit Quality”

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

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.

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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DexLab Analytics offer the best credit risk analysis course.

Pointed things to learn from a fintech startup in India:

Product is king, so is its timing – Never ever compromise with a good product. Similarly, make sure the timing is right too – may be, because you waited too long, you missed the best product. It happens.

Hit the customers right away – Don’t vouch for any product, unless 10 people have validated the product. Allow at least 10 customers to use that product and then sit with them to grab some feedback. Startups work like this, so do you!

Economics is the essence, so do proper homework – Risk and Finance go hand in hand, but are distinct in nature. Get a grip on well-structured financial models – they will help you understand the credit exchange stuffs better. Streaming costs, revenues and growth in a single line will obviously put you in a better position in predicting the impact of credit risk. FYI, credit risk’s impact is endured on not only losses, but on costs too – which is surely a matter of concern.

Teamwork is the best work – Building a potent team is an art. Creating something of your own requires a substantial amount of risk, both personal and professional. Most seasoned consultants coming under a single roof to offer something unique is in itself an exciting idea – startups in India boast of an average age of 25 or 28 years in a particular company. Nevertheless, some companies also excel with a core team whose average experience is that of 10 years – across domains like tech, product, risk, operations, sales and marketing. The figures are interesting, ain’t they?

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Fintech is more finance and less technology – As compared to other industries, fintechs’ operational mode is very different.  Though credit risk and cost management are the founding pillars of a robust fintech business setup, none of them can make up for below-standard quality products. Offering high quality product is of supreme importance for the success of any Fintech, and if you look at fintech companies in the US and Europe you will understand why we are focusing our attention on the quality part.

While we are on the closure, there is still a lot of learning to be done – but we surely believe India is on its way to success and our fintech sector is witnessing a plethora of amazing ideas. Just keep your fingers crossed, and hope our teams pull it off in a snap.

Get credit risk modelling certification from DexLab Analytics today! Their credit risk management courses are intensive, well-researched and are written down, while keeping students’ grasping skills in mind. Go give it a shot!

 


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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.

Understanding Credit Risk Management With Modelling and Validation – @Dexlabanalytics.

SYSTEMATICALLY IMPORTANT FINANCIAL INSTITUTIONS AND THEIR ROLE IN SYSTEMIC STABILITY

A Systematically Important Financial Institution (SIFI) is a bank, insurance company, or other financial institutions whose failure might trigger a financial crisis.

If a SIFI has the capacity to bring in a recession across the globe then it is known as a Globally Systematically Important Financial Institution (G-SIFI). The Basel Committee follows an indicator based approach for assessing the systematic importance of the G-SIFIs. The basic tenets of this approach are:

  1. The BASEL committee is of the view that the global systemic importance should be measured in terms of the impact that a failure of a bank can have on the global financial system and wider economy rather than the risk that the failure can occur. So, the concept is more of a global, system wide, loss given default (LGD) concept rather than a probability of default (PD) problem.
  2. The indicators reflect the following metrics: size of banks, their interconnectedness, the lack of availability of substitutable or financial institution infrastructure for provided services, their global activity, their complexity etc. Each of these are defined as:

(i) Cross-Jurisdiction: The indicator captures the global footprints of the banks. This indicator is divided into two activities: Cross Jurisdictional claims and Cross Jurisdictional liabilities. These two indicators measure the banks activities outside its home relative to overall activity of other banks’ in the sample. The greater the global reach of the bank, the more difficult is it to coordinate its resolution and the more widespread the spill over effects from its failure.

(ii) Size: Size of a bank is measured using the total exposure that it has globally. This is the exposure measure used to calculate Leverage ratio. BASEL III paragraph 157 uses a particular definition of exposure for this purpose. The score of each bank for this criterion is calculated as its amount of total exposure divided by the sum of total exposures of all banks in the sample.

(iii) Interconnectedness: Financial distress at one institution can materially raise the likelihood of distress at other institutions given the contractual obligations in which the firms operate. Interconnectedness is defined in terms of the following parameters: (a) Inter-financial system assets (b) Inter-financial system liabilities (c) The degree to which a bank funds itself from the other financial systems.

(iv) Complexity: The systemic impact of a bank’s distress or failure is expected to be positively related to its overall complexity. Complexity includes: business, structural and operational complexity. The more complex the bank is the greater are the costs and time needed to resolve the banks.

Given these characteristics, it was important to apply different restrictions to keep the lending practices of the banks under control. Frivolous lending done by such SIFIs had resulted in the financial crisis 2008-09. Post the crisis, regulators became more vigilant about maintaining appropriate reserves for banks to survive macroeconomic stress scenarios. Three major sources of risks to which banks are exposed to are: 1. Credit Risk 2. Market Risk 3. Operational Risk. Several regulations

have been imposed on banks to ensure that they are adequately capitalised. The major regulatory requirements to which banks need to be compliant with are:

  1. BASEL 2. Dodd Frank Act Stress Testing 3. Comprehensive Capital Adequacy Review.

Before looking into the Regulatory frameworks and their impact on the Credit Risk modelling, let us form an understanding of the framework of the Bank Capital.

Risk Management in a Commercial Lending Portfolio with Time Series and Small Datasets – @Dexlabanalytics.

CAPITAL STRUCTURE OF BANKS

The bank’s capital structure is comprised of two main components: 1. Equity Capital of Banks 2. Supplementary capital of banks. The Equity capital of banks are the purest form of banking capital. This is true or the actual capital that a bank has and it has been raised from the shareholders. The supplementary capital of banks comprises of estimated capital such as allowances, provisions etc. This portion of the capital can easily be tampered by the management to meet undue shareholders expectations or unnecessarily over reserve capital. Thus, there are strong capital norms and regulations around the supplementary capital. The two tiers of capital are: Tier1 and Tier2 capital. Tier1 capital is also decomposed into two parts: Tier1 Common capital and Tier1 capital.

 

Tier1 common capital = Common shareholder’s equity-goodwill-Intangibles. Goodwill and intangibles are no physical capital. In scenarios, where the goodwill and intangible assets are stressed, the capital in the banks would deteriorate. Therefore, they cannot be added to the company’s tier1 capital. Only the core or the physical amount of capital present in the bank account is the capital.

Tier1 Capital = Total Shareholders’ equity (Common + Preffered stocks) -goodwill -intangibles + Hybrid securities.

Tier 1 is the core equity capital for the bank. The components of Tier1 capital are common across all geographies for the banking system. Equity capital includes issued and fully paid equities. This is the purest form of capital that the bank has.

Tier2 Capital: tier 2 capital comprises of estimated reserves and provisions. This is the part of capital which is used to cushion against expected losses. Tier 2 capital has the following composition:Tier 2 = Subordinated debts +Allowances for Loans and lease losses + Provisions for bad debts -> This portion of the capital is reserved out of profits. Hence,

managers always try to under report these parameters to meet shareholder’s expectations. However, under reserving often poses the chances of bankruptcies or regulatory penalties. Total Capital of a Bank = Tier 1 capital + Tier 2 Capital

Explaining the Everlasting Bond between Data and Risk Analytics – @Dexlabanalytics.

CALCULATION OF CAPITAL RATIOS

Every bank faces three main types of risks: 1. Credit risk 2. Market Risk 3. Operational risk. Credit Risk is the risk that arises from lending out funds to borrowers, given their chances of defaulting on loans. Market Risk is the risk that the bank faces due to market fluctuations like stock price changes, interest rate risk and price level fluctuation etc. Operational risk occurs as a failure of the operational processes. The exposure of the banks to these risks differ from bank to bank. So the capital that they to set aside would differ based on the exposure to risk. Therefore, regulators have defined a metric called Risk Weighted Assets (RWA) to identify the exposure of the bank’s assets to risk. Every bank must keep aside their capital relative to the exposure of their asset to risk. The biggest advantage of RWAs is that they not only include On-balance sheet items but off-balance sheet items as well. Banks need to maintain their Tier1 common capital, tier1 capital and tier2 capital relative to their RWAs. Thus, arises the Capital ratios.

 

Total RWA = RWA for Credit Risk + RWA for Market Risk + RWA for Operational Risk

Tier1 Common Capital Ratio = tier1 common capital / RWA (CR + MR + OR)

Tier1 Capital Ratio = Tier1 Capital / RWA (CR+MR+OR)

Total Capital Ratio = Total capital/ RWA(CR+MR+OR)

Leverage Ratio = Tier1 Capital / Firms consolidated assets

Regulators require some critical cut-offs for each of these ratios:

Tier1 Common Capital Ratio > = 2% all times

Tier1 Ratio >= 4% all times

Tier 2 capital cannot exceed Tier1 capital

Leverage ratio > = 3% of all times.

 

In the next blog we explore how the credit risk models help in ensuring the capital adequacy of the banks and in the business risk management.

 

Looking for credit risk analysis course online? Drop by DexLab Analytics – it offers excellent credit risk analysis course at affordable rates.

 

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Sources Of Banking Risks: Credit, Market And Operational Risks

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:


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There are three broad sources from which banking risks originate: 1. Credit Risk 2. Market Risk 3. Operational Risk.

CREDIT RISK:

Credit Risk arises when the borrower defaults to honour the repayment commitments on their debts. Such a risk arises as a result of adverse selection (screening) of applicants at the stage of acquisitions or due to a change in the financial capabilities of the borrower over the process of repayment. A loan will default if the borrower’s assets (A) at maturity (T) falls below the contractual value of the obligations payable (B) (Vasicek,1991). Let A_i be the asset of the i-th borrower, which is described by the process:

MARKET RISK:

Market Risk includes the risk that arises for banks from fluctuation of the market variables like: Asset Prices, Price levels, Unemployment rate etc. This risk arises from both on-balance sheet as well as off-balance sheet items. This risk includes risk arising from macroeconomic factors such as sharp decline in asset prices and adverse stock market movements. Recessions and sudden adverse demand and supply shock also affect the delinquency rates of the borrowers. Market Risk includes a whole family of risk which includes: stock market risks, counterparty default risk, interest rate risk, liquidity risk, price level movements etc.

OPERATIONAL RISK:

Operational Risk arises from the operational inefficiencies of the human resources and business processes of an organisation. Operational risk includes Fraud risks, bankruptcy risks, risks arising from cyber hacks etc. These risks are uncorrelated across the industries and is very organisation specific. However, Operational risk excludes strategy risk and reputation risk.

This blog is the continuation of the first blog, which was on the topic – The Basics of the Banking Business and Lending Risks. To read the blog, click here ― www.dexlabanalytics.com/blog/the-basics-of-the-banking-business-and-lending-risks

Stay glued to our site for further details about banking structure and risk modelling. DexLab Analytics offers a unique module on credit risk analysis training in Bangalore.

 

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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:

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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.

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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.

Amidst all, cloud computing grabbed the utmost attention. The benefits of cloud computing are myriad – better productivity, lower costs and quicker time to market. A surging number of employees are using cloud applications to talk about various work-related subject matters. Nevertheless, data security is still a leading concern.

 

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Traditional threats are no more potent. Most organisations have devised manipulating ways to safeguard themselves against those predictable threats, newer threats call for better IT security to realise high profile business priorities. A well-researched study by VMware, a pioneer in cloud infrastructure and digital workspace technology revealed that though businesses – small, medium and large will be more than keen to implement cloud computing to secure better future goals and efficiency, information security thriving on the cloud will have a profound impact on enterprises in the next 3-5 years.

 

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The Cloud Security

Another study by eminent research firm Kantar IMRB highlighted that though organisations are taking steps towards a modern workspace environment, they are more interested about having a safe and secured digital environment, thanks to a rising number of cyber threats and thefts. If you follow the figures, in the next 3-5 years, more than 86% of enterprises are going to enhance their IT Budget and 80% of organisations will be eager to expend more time, skill and money on cloud technology.

 

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In respect to the above context, Arun Parameswaran, managing director of VMware India said, “With nearly 25% of all IT workloads being managed on the cloud today, and the number expected to double by 2021, it is evident that the traditional on-premises IT environment is undergoing a profound change.” He further added, “Today, CIOs play an extremely essential role in their organisations’ IT, and it is of utmost importance to have enterprise data available always—anytime and anywhere while tightly secured.”

Enhanced productivity and better profitability will always remain a prime priority, but now as per the recent studies, IT security has also become a chief concern in the list of business priorities. However, despite heavy investments in IT, CIOs of well-established companies are unhappy because the budget is either not structured properly or inadequate. The studies also reveal that the government and BFSI respondents think that the budget for IT security is quite low, and it should be increased at least by 25% by next year.

 

Cloud is the best thing since sliced bread. Companies are relying more on cloud to store sensitive data. Cloud is the future; so companies should look up to ways to balance the risks with explicit advantages that this evolving technology brings in.

 Data-Privacy

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