Credit and Market Risk Archives - Page 4 of 5 - DexLab Analytics | Credit Risk | Market Risk | SAS Python Machine Learning Modeling

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.

Continue reading “Analyze the Risk of a Borrower with These Sure-fire Credit Risk Analytics Techniques”

Incredible Future Possibilities of Market Risk Analytics

Global risks are burgeoning; companies of all sizes are seeking the perks of risk analytics and management. Smart companies are realizing the change is coming from people as well as recent technological breakthroughs, including Big Data and AI. And CEOs are improvising their risk teams, and transforming them into perceptive strategic advisors to address budding dangerous threats like cybercrime.

 
Incredible Future Possibilities of Market Risk Analytics
 

Modern risk analysts have accurate knowledge about risk, artificial intelligence and cyber security – so, it’s time they get an opportunity to show a greater presence in the stoic boardrooms as strategic advisors. AI, the cutting-edge risk analytics tool surfaced out to enhance the inexorable march of big data. As such, their importance in the organization in assessing risk has greatly increased.

Continue reading “Incredible Future Possibilities of Market Risk Analytics”

Market Risk Management 101: Types of Market Risks and How to Manage Them

Market Risk Management 101: Types of Market Risks and How to Manage Them

Last year, Britain opted to leave the European Union – and that created spiking fluctuation and acute market uncertainty across the globe.

Most of the investors out there know investment involves risks and rewards, just like head and tail in a coin and so do the analysts. Higher the risk, better are the chances to gain potential rewards. As a result, it is critical for both an investor and analyst to understand the true nature of market risks that influences the market conditions and controls the shooting volatility and the ways to manage those risks.

Data Science Machine Learning Certification

Common Market Risks

Relevant market risks depend largely on the nature of investment as well as geographic boundaries. Some of the key market risks are as follows:

  • Interest Rate Risk – It is the risk of a decrease in the value of a security owing to changes in interest rates. The rate of change of interest rates is inversely proportional to bonds – based on a rationale that a bond is the future security of a healthy stream of payments – hence as interest rate rises, the price of the issued bonds decreases.

35e54105baeebae9f5da06c73db56683--market-risk-finance-blog

  • Inflation Risk – It relates to the risk that gets affected as the prices of goods and services increases reducing the value of money. This risk results in affecting the value of investments in a negative way. It decreases the purchasing power of money, thereby reducing the value of investment. Sometimes inflation risk is also known as Purchasing Power Risk.
  • Currency Risk – This type of risk arises when your money needs to be converted to a different currency for investment purposes. Here, a small change in exchange rates between the home currency and US dollars can affect your investment return.
  • Liquidity Risk – It refers to the risk of not being able to fulfill certain investment requirements quickly for a price that determines the true value of the asset. Sometimes, one may face difficulties in selling the investment due to a lack of buyers, resulting in a drastic decrease of investment value of that product until someone is ready to pay for it. Foreign investments, over-the-counter markets and small-capitalization stocks are some of the high liquidity risks items.
  • Sociopolitical Risk – The socio-political environ, such as war, terrorist attack, election and corruption affects the market conditions. They affect investor perceptions, resulting in severe oscillation in stock prices.

Managing Market Risk

Well, you can’t control the market risks from taking a front seat in your financial life, though you can take some steps to manage and mitigate them.

forex-education

As globalization seeped through all leading economies and market segments, a majority of fintech institutions started realizing the criticality of an enhanced operational risk, especially related to cyber-security, IT failures and data theft. Amid this, cyber risks and data theft issues posed key challenges, followed by IT failures and outsourcing issues. The revolution of digitization did many goods to our society, but the moment banks got dependent on single computer networking setups, the vulnerability of confidential customer data leakage multiplied. As a result, the need for data analysts and market researchers spiked up – they are the trained souls who possess both the experience and expertise to tackle diverse investment portfolios for clients in the best way possible to fetch maximum profits.

For that, affluent market risk courses in Delhi are available around – train your mind well, before taking the big leap in the big field of data analytics. Once you are done, reach DexLab Analytics – their comprehensive Market Risk Modelling using SAS courses are top-of-the-line courses in the industry at present.

Catch market risk modeling demo session here,

 

Interested in a career in Data Analyst?

To learn more about Market Risk Modeling course – Enrol Now.
To learn more about Advanced Credit Risk Modeling Course – Enrol Now.
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.

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.

shutterstock_631603844

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?

55508629_custom-750x418

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!

 


.

Market Risk Analytics: How Top Notch Companies Are Assessing Intricate Risks​

Risk analytics tools boost operational efficiency. But do you know what tools to implement to derive the best results?

 
Market Risk Analytics: How Top Notch Companies Are Assessing Intricate Risks
 

With the burgeoning demand for big data all over the world, major corporate houses are taking risk analytics – the process of collecting, analyzing and measuring real-time data to forecast future risk for improved decision-making – to a new high.

Continue reading “Market Risk Analytics: How Top Notch Companies Are Assessing Intricate Risks​”

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.

 

Interested in a career in Data Analyst?

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

Market Risk Analytics: What It is All About

Market Risk Analytics: What It is All About

With time, firms need more efficient, versatile and highly functional analytics tools to address new, complex issues related to market risk. Market risk analytics involve a comprehensive set of integrated, scalable and productive solutions for wide-range risk management across various verticals of asset classes.

A New Course Alert! DexLab Analytics Launches Market Risk Analytics and Modelling – @Dexlabanalytics.

Why Risk Analytics?

Risk analytics basically help organizations realize the existence of risks lying under business activities – by facilitating enterprises to identify, determine and manage their company risk. In lieu of this, the pressing need for risk analytics is going to increase across industries in the coming few years. New developments, like real-time risk analytics, which is an advanced form of traditional risk analytics process that calculates risk on a real-time basis, are influencing the entire market, while accentuating its mitigating abilities.

DexLab Analytics Introduces Market Risk Analytics and Modelling Online Session – @Dexlabanalytics.

What the Course Offers?

Many top notch education-providing companies are now offering Market Risk Analytics and Modelling online course to better alleviate and handle risks. Increasing needs to address particular risk-induced challenges and excessive focus on the financial market sector is driving the risk analytics market in India. Hence, learning and honing your skills on market risk is indispensable – DexLab Analytics brings Predictive modelling of market risk using SAS to India. The course module will address key issues, like the different types of risks faced by banks, the 1990’s financial crisis, sources and scope of market risk, theoretical probability distributions, volatility forecasting and clustering models, value at Risk Modelling, quantitative models of market risk and description of key financial products.

Some of the most common types of risks that banks are exposed to are Credit risk, Market risk, Operational risk, Liquidity risk, Business risk, Reputational risk, Systemic risk and Moral hazard. All banks need to establish separate risk management departments to manage, monitor and mitigate such high-flying risks. The concept of probability distributions sheds light on investing options – stock returns are expected to be distributed normally, but the reality may vary. They are mostly used in risk management to determine the probability of an event as well as the proportion of losses that it would strike based on a distribution of historical returns. Clustering models is another branch of risk analytics that helps in identifying groups of similar records and marking the records in accordance to the group in which it belongs. These models are also known as unsupervised learning models. Apart from this, other valuable concepts will be addressed during the online live sessions.

Closing Thoughts

Emergence of real time risk analytics is boosting the market of risk analytics. Technology being the driving factor for real-time analysis trades data to the organizations to balance market volatility. Leading service providers are on their quest to design and develop dynamically configurable risk analytics frameworks for clients. And why not, risk analytics boasts of widespread applications, starting from fraud detection to liquidity risk analysis, credit risk management and product portfolio management – various industries are nowadays looking up to market risk analytics, including banking, financial services, government, healthcare, insurance, manufacturing, transportation and logistics, consumer goods and retail, energy and utilities, telecommunication and information technology (IT), media and entertainment, and many others.

Reach us at DexLab Analytics for over-the-top SAS risk management certification course. Their courses are truly remarkable and perfect to take a step into the world of analytics.

 

Interested in a career in Data Analyst?

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

A New Course Alert! DexLab Analytics Launches Market Risk Analytics and Modelling

We are back again with some great news! Technology enthusiasts and hardcore industry professionals got another reason to cheer for DexLab Analytics, as we feel extremely delighted to announce our new Market Risk Analytics and Modelling online live sessions. We welcome hundreds and thousands of young, aspiring data enthusiasts from various parts of the country who are driven by hunger, passion and robust dreams of a data-friendly future to get enrolled in our online course on Market Risk Analytics using SAS. In our quest for expanding our horizons, these types of analytics course play a significant role.

 
A New Course Alert! DexLab Analytics Launches Market Risk Analytics and Modelling
 

Recently, Market Risk Analytics have gained a lot of prominence – a lot of tech pundits and industry practitioners have repeatedly emphasized on the importance of having sound market risk management policies and strong internal controls. Especially, since the global financial crisis, the critical aspect of risk management analytic has doubled.

Continue reading “A New Course Alert! DexLab Analytics Launches Market Risk Analytics and Modelling”

What the Future Holds for Risk Management in Banking

The past decade saw some impressive changes brought into the aorta of risk management. And the change is showing no signs of slowing down, now.

 
What the Future Holds for Risk Management in Banking
 

In order to keep pace with the changing times, you need to get to the crux of these five trends that are shaping the role of risk management in banking sector: Continue reading “What the Future Holds for Risk Management in Banking”

Call us to know more