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## A Beginner’s Guide to Credit Risk Modelling

When a lender starts a financial institution with the aim of lending money to entities, he is most strongly fortified against credit risk. He undertakes several measures to lower credit risk and this is called credit risk modelling.

“A good credit risk assessment can prevent avoidable losses for an organization. When a borrower is found to be a debtor, it could dent their creditworthiness. The lender will be skeptical about offering loans for fear of not getting it back,” says a report.

Credit risk assessment is done to gauge whether a borrower can pay back a loan. The credit risk of a consumer is determined by the five Cs – capacity to repay, associated collateral, credit history, capital, and the loan’s conditions.

“If a borrower’s credit risk is high, their loan’s interest rate will be increased. Credit risk shows the likelihood of a lender losing their loaned money to a borrower.”Credit risk highlights a borrower’s ability to honour his contractual agreements and repay loans.

“Conventionally, it deals with the risk every lender must be familiar with, which is losing the principal and interest owed. The aftermath of this is a disturbance to the lender’s cash flow and the possibility of losing more money in a bid to recover the loan.”

#### Credit Risk Modelling

While there is no pronounced way to determine the credit risk of an individual, credit risk modeling is an instrument that has largely come to be used by financial institutions to accurate measure credit risk.

“Credit risk modeling involves the use of data models to decide on two important issues. The first calculates the possibility of a default on the part of a loan borrower. The second determines how injurious such default will be on the lender’s financial statement.”

#### Financial Statement Analysis Models

Popular examples of these models include Moody’s RiskCalc and Altman Z-score. “The financial statements obtained from borrowing institutions are analyzed and then used as the basis of these models.”

#### Default Probability Models

The Merton model is a suitable example of this kind of credit risk modeling. The Merton model is also a structural model. Models like this take into account a company’s capital structure “because it is believed here that if the value of a company falls below a certain threshold, then the company is bound to fail and default on its loans”.

#### Machine Learning Models

“The influence of machine learning and big data on credit risk modeling has given rise to more scientific and accurate credit risk models. One example of this is the Maximum Expected Utility model.”

#### The 5Cs of Credit Risk Evaluation

These are quantitative and qualitative methods adopted for the evaluation of a borrower.

1. #### Character

“This generally looks into the track record of a borrower to know their reputation in the aspect of loan repayment.”

1. #### Capacity

“This takes the income of the borrower into consideration and measures it against their recurring debt. This also delves into the borrower’s debt-to-income (DTI) ratio.”

1. #### Capital

The amount of money a borrower is willing to contribute to a potential project can determine if the lender will lend him money.

1. #### Collateral

“It gives the lender a win-win situation, in the sense that upon a default, the lender can sell the collateral to recover the loan.”

1. #### Conditions

“This takes information such as the amount of principal and interest rate into consideration for a loan application. Another factor that can be considered as conditions is the reason for the loan.”

#### Conclusion

There is no formula anywhere that exposes the borrower who is going to default on loan repayment. However, the proper assessment of credit risk can go a long way in reducing the impact of a loss on a lender. For more on this, do visit the DexLab Analytics website today. DexLab Analtyics is a premiere institute that provides credit risk analysis courses online.

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## Credit Risk Modeling: A Comprehensive Guide

Credit Risk Modeling is the analysis of the credit risk of a borrower. It helps in understanding the risk, which a lender may face when he offers a credit.

#### What is Credit Risk?

Credit risk is the risk involved in any kind of loan. In other words, it is the risk that a lender runs when he lends a sum to somebody. It is thus, the risk of not getting back the principal sum or the interests of it on time.
Suppose, a person is lending a sum to his friend, then the credit risk models will help him to assess the probability of timely payments and estimate the total loss in case of defaulters.

#### Credit Risk Modelling and its Importance

In the fast-paced world of now, a loss cannot be afforded at any cost. Here’s where the Credit Risk Modeling steps in. It primarily benefits the lenders by accurate approximation of the credit risk of a borrower and thereby, cutting the losses short.

Credit Risk Modelling is extensively used by financial institutions around the world to estimate the credit risk of potential borrowers. It helps them in calculating the interest rates of the loans and also deciding on whether they would grant a particular loan or not.

#### The Changing Models for the Analysis of Credit Risks

With the rapid progress of technology, the traditional models of credit risks are giving way to newer models using R and Python. Moreover, credit risk modeling using the state-of-the-art tools of analytics and Big Data are gaining huge popularity.

Along with the changing technology, the advancing economies and the successive emergence of a range of credit risks have also transformed the credit risk models of the past.

#### What Affects Credit Risk Modeling?

A lender runs a varying range of risks from disruption of cash flows to a hike in the collection costs, from the loss of interest/interests to losing the whole sum altogether. Thus, Credit Risk Modelling is paramount in importance at this age we are living. Therefore, the process of assessing credit risk should be as exact as feasible.

However, in this process, there are 3 main factors that regulate the risk of the credit of the borrowers. Here they are:

1. The Probability of Default (PD) – This refers to the possibility of a borrower defaulting a loan and is thus, a significant factor to be considered when modeling credit risks. For the individuals, the PD score is modeled on the debt-income ratio and existing credit score. This score helps in figuring out the interest rates and the amount of down payment.
2. Loss Given Default (LGD) – The Loss Given Default or LGD is the estimation of the total loss that the lender would incur in case the debt remains unpaid. This is also a critical parameter that you should weigh before lending a sum. For instance, if two different borrowers are borrowing two different sums, the credit risk profiles of the borrower with a large sum would vary greatly to the other, who is borrowing a much smaller sum of money, even though their credit score and debt-income ratio match exactly with each other.
3. Exposure at Default (EAD) – EAD helps in calculating the total exposure that a lender is subjected to at any given point in time. This is also a significant factor exposing the risk appetite of the lender, which considerably affects the credit risk.

#### Endnotes

Though credit risk assessment seems like a tough job to assume the repayment of a particular loan and its defaulters, it is a peerless method which will give you an idea of the losses that you might incur in case of delayed payments or defaulters.

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## How Machine Learning Technology is Enhancing Credit Risk Modeling

Risk is an intrinsic part of the money lending system. There’s always the chance that customers borrowing money from financial institutions fail to repay their loans. And to determine the exact probability of a customer paying off a loan or defaulting on it, banks and other lenders rely on credit risk modeling.

#### Next-Gen Credit Assessment Techniques

The credit situation has changed a lot from how it used to be ten years ago. And to keep up, lenders must also evolve by identifying and responding to issues in real-time.  Credit risk strategy has become more complex and multiple factors need to be weighed to arrive at the correct decision that’s both profitable for the enterprise and customer. Sophisticated models that contain more than one dimension, such as additional information about a customer’s finance and behavior patterns, are in demand. These models help get a 360 degree view of the customer’s financial condition.

Moreover, banks want to provide broader financial inclusion with the intention that more customers get credit scores and avail their financial services. But they need to keep a check on their risk levels too. Traditional credit assessment techniques having linear nature, for example logistic regression, are useful, but only till a point.

#### Neural Networks

Recent developments in neural networks have greatly improved credit risk modeling and seem to provide a solution to the above mentioned problem. One such breakthrough is the NeuroDecision Technology from Equifax that facilitates more inclusive models, so scores and consent can be given to a bigger and varied group of customers.

Machine Learning (ML) is a fast-moving field and neural networks are used within deep learning, which is an advanced form of ML. It has the potential to make more accurate predictions and go beyond the linear analysis methods of logistic regression.  This is a positive development for both the business and its customers.

#### Linear Vs. Inclusive

What happens in a logistic regression model is that all customers above a straight line (prime) get approved, whereas everyone falling below that line (subprime) gets rejected. Hence, customers who are working hard towards creating a good credit profile but fall just below prime get declined repeatedly. Despite this problem, traditional linear models are widely used because outcomes can be easily conveyed to customers, which helps to be in sync with consumer credit regulations that demand higher transparency.

On the other hand, neural networks lead to non-linear or curved arcs that include those customers who aren’t yet prime, but are evidently moving in the right direction. This increases the ‘approved customer’ base, which is beneficial for the business because customers are being served better and the enterprise is growing. This model is advantageous from the perspective of customers also as it allows more people to access mainstream financial services.  The only problem is explaining the outcome to customers as neural networks tend to be rather complex.

#### Concluding Note

Many companies are producing robust credit modeling tools employing deep learning techniques. And these game-changing developments highlight the fact that they are just the starting point of a series of interesting developments ahead.

You can be a part of this exciting and booming field too! Just enroll for credit risk modeling certification at DexLab Analytics. Detailed courses chalked out and taught by industry professionals ensure that you get the best credit risk training in Delhi.

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## Role of Chief Risk Officers: From Managing Only Credit Risks to Playing Key Roles in Big Banks

The job responsibilities of chief risk officers (CROs) have evolved drastically over the last two decades. CROs are playing key profitable roles is some of the world’s biggest banks. In the face of the global financial crisis, risk departments, particularly CROs, are handling many more tasks apart from what they were managing twenty years back, like modeling credit and market risks and avoiding fines and criminal investigations. The list of responsibilities entrusted to the CROs has grown exponentially since the last two decades. Operational risk that are quantifiable through capital necessities and penalties for nonconformity was actually developed from a set of unquantifiable ‘’other’’ risks.

#### Cyber risk:

In the present times, cyber risk has become one of the most pressing global problems that the risk departments need to cope with. The number of cyber hacks is on the rise, wreaking havoc on daily lives as well as social settings. For example, Bank of America and Wells Fargo were among the major institutes hit by the DDoS attack of 2012. It is one of the biggest cyber attacks till date, which affected nearly 80 million customers. In 2016, Swift hack was only a typo away from disrupting the global banking network.

‘’What is called ‘operational resilience’ has spun out of business continuity and operational risk, financial crime, technology and outsourcing risk- anything with risk in the title, somehow there is an expectation that it will gravitate to risk management as their responsibility,’’ says Paul Ingram, CRO of Credit Suisse International. The array of responsibilities for a CRO is so immense, including regulatory compliance, liquidity risk, counterparty risk, stress-test strategy, etc, that it is imperative for the CRO to be a part of the board of directors.

Previously, CROs reported to finance director; now they are present on the board itself. They are playing crucial roles in forming strategies and executing them, whereas around two decades ago they were only involved in risk control. The strategies should be such that the capital allocated by the board is utilized optimally, neither should the limits be exceeded nor should it be under-utilized. CROs add value to the business and are responsible for 360 degree risk assessment across the entire bank.

Banks are tackling problems like digital disruption, rising operational costs and increased competition from the non-banking sector. CROs play a crucial role in helping banks deal with these issues by making the best use of scarce resources and optimizing risk-return profiles.

#### Regulatory attack:

‘’Since the crisis, CROs have had their hands full implementing a vast amount of regulation,’’ says BCG’s Gerold Grasshoff. However, regulation has almost reached its apex, so CROs must now use their expertise to bring in more business for their institutions and help them gain a competitive advantage. CROs need to play active roles in finding links between the profits and losses of their businesses and balance sheets and regulatory ratios.

Risk departments were once the leaders in innovations pertaining to credit and market risk modeling. They must utilize the tactics that kept them at the forefront of innovation to help their institutions generate improved liquidity, asset and fund expenditure metrics. Their skill in spotting, checking and gauging risk is essential to provide risk-related counsel to clients. Risk departments can team up with Fintechs and regtechs to improve efficiencies in compliance and reporting sections and also enable digitizing specific risk operations.

Thus risk departments, especially CROs can add a lot of value to the banking infrastructure and help steer the institutes forward.

Credit risk modeling is an essential part of financial risk management. To develop the necessary knowledge required to model risks, enroll for credit risk analytics training at DexLab Analytics. We are the best credit risk modeling training institute in Delhi.

## To Be Ahead of the Curve: Banks Must Beef Up Technology

Technology is critical. To improve efficiency, reduce costs, stay on the cutting edge over tailing rivals, fulfill customer requirements and initiate a proper risk management process, technology is an incredible tool to possess.

The abovementioned facts received momentum at the SAS Risk & Finance Analytics Roadshow in Lagos, during which it was inferred that the banks nowadays are adapting themselves to regulatory changes, thus reducing costs in no time.

In this context, Charles Nyamuzinga, Senior Business Solutions Manager, Pre-Sales Risk Practice, stated that banks in Africa need to confront with additional challenges, including risk analytics skills gaps, challenges associated with data management and integrating finance and risk management nuances across an organization.

“But, on the positive side, they have started considering technology as a way of eliminating these challenges, and have access to new streams of data that are also helping to advance the financial inclusion mandate,” he noted.

In compliance with global financial norms, African banks should by now be compliant with the new IFRS9 Accounting Standard, which comes with some changes in the way expected credit losses used to be calculated.

“There is also need to start thinking about the new ‘Basel IV’ framework, which impacts on how banks calculate their risk weighted assets, and the amount of capital they need to offset those risks,” he added.

According to Charles, banks are feeling intense regulatory pressure nowadays, while tussling with daily requirements, challenges and questions associated with taking stress tests. The regulators have become severe on stress testing processes, and that may be for good! Besides, banks need to worry about the effect on reputation, capital shortfalls and negative influence on earnings, along with non-compliance penalties.

His concern was thoroughly evident in these statements, “There’s a good chance that banks in Africa could get this wrong if they use disparate and fragmented systems for data management, model building and implementation and reporting – which is often the case – or if they try to do the computations manually.

“The biggest causes of incorrect modeling are data management and quality issues and skills shortages. Banks have to obtain and analyze enormous amounts of detailed data, for example. And, to comply with IFRS 9, banks must look at millions of customers with hundreds of data points.”

In support of the above observations, SAS Sales Manager, West Africa, Babalola Oladokun raised concerns if a bank ends up miscalculating a customer’s credit score, it would result in giving a loan to someone, who for sure won’t be able to repay it. This can have serious implications for IFRS 9expected credit loss calculations. Furthermore, if a bank lacks in capital on hand to offset the loan deficiency, the case will go straightaway to Basel Capital requirements compliance issues.

“Data gathering and manipulation from disparate data sources wastes time and resources that banks could have used to develop new products and find more convenient ways to serve their customers – something their competitors in the FinTech space are very good at,” he noted.

As last thoughts, FinTechs use virgin data streams to draw instant conclusions and fuel decision-making processes for customers. For an example, they base their inferences about granting a loan to someone who doesn’t even have a bank account – surely, this is an innovative way to give non-banking population access into the world of finance.

If finance and big data interests you, we suggest you go through our credit risk management courses in Delhi. DexLab Analytics is not only a trailblazer in credit risk modelling courses, but also a robust platform for training young minds.

## Developing a Big Data Culture is Crucial for Banks to be Customer Centric

It is important for banks to be customer centric because a customer who is better engaged is easier to retain and do business with. In order to provide services that are valued by customers, banks need to exploit big data. When big data is combined with analytics it can result in big opportunities for banks. The volume of banking customers is on the rise, and so is their data. It is time for the banking sector to look beyond traditional approaches and adopt new technologies powered by big data, like natural language processing and text mining, which help convert large amount of unstructured information into meaningful data that can lead to valuable insights.

Switching to big data enable banks to get a 360-degree view of their customers and keep providing excellent services. Many banks, like the Bank of America and U.S. Bank, have implemented big data analytics and are reaping its benefits. Rabobank, which has adopted big data analytics to detect criminal activity in ATMs, is ranked among the top 10 safest banks in the world.

#### Big Data’s Advantages for the Banking Industry:

• Streamline Work Process and Service Delivery:

Banks need to filter through gazillions of data sets in order to provide relevant information to a customer, when he/she enters account details into the system. Big data can speed up this process. It enables financial institutes of spot and correct problems, before they affect clients. Big data also helps in cost-cuttings, which in turn lead to higher revenues for banks.

In case of erroneous clients, who tend to go back on their decisions, big data can help alter the process of  service delivery in such a manner that these clients are bound to stick to their commitments. It allows banks to track credit and loan limits, so that customers don’t exceed them.

Cloud based analytics packages sync in with big data systems to provide real-time evaluation. Banks can sift through tons of client information to track transactional behaviors in real time and provide relevant resources to clients. Real-time client contact is very useful in verifying suspicious transactions.

• Customer Segmentation:

Big data help banks understand customer spending habits and determine their needs. For example, when we use our credit cards to purchase something, banks acquire information about what we purchase, how much we spend and use these information to provide relevant offers to us. Through big data, banks are able to trace all customer transactions and answer questions about a customer, like which services are commonly accessed, what are the preferred credit card expenditures and what his/her net worth is. The advantage of customer segmentation is that it enables banks to design marketing campaigns that cater to specific needs of a customer.  It can be used to deliver personalized schemes and plans. Analyzing the past and present expenses of a client helps bank create meaningful client relationships and improve response rates.

• Fraud detection:

According to Avivah Litan, a financial fraud expert at Gartner, big data supports behavioral authentication, which can help prevent fraud. Litan says, ‘’using big data to track such factors as how often a user typically accesses an account from a mobile device or PC, how quickly the user types in a username and password, and the geographic location from which the user most often accesses an account can substantially improve fraud detection.’’

Utah-based Zions Bank is largely dependent on big data to detect fraud. Big data can detect a complex problem like cross-channel fraud by aggregating fraud alerts from multiple disparate data sources and deriving meaningful insights from them.

• Risk Management:

Financial markets are becoming more and more interconnected, which increases their risk. Big data plays a pivotal role in risk management of financial sector as it provides more extensive risk coverage and faster responses. It helps create robust risk prediction models that evaluate credit repayment risks or determine the probability of default on loans for customers. It also aids in identifying risk associated with emergent financial technologies.

Hence, banks need to adopt a big data culture to improve customer satisfaction, keep up with global trends and generate higher revenues.

For credit risk management courses online, visit DexLab Analytics. It is a leading institute offering credit risk analytics training in Delhi.

## A Comprehensive Article on the Trends, Dynamics and Developments of Risk Analytics Market

Risk analytics makes organizations aware of the potential risks in their businesses. It helps companies make risk-aware decisions and improves their overall business performance. Risk analytics tools help investors get a better return on their capital and minimize the money required to be spent on regulatory compliances. Risk analytics tools aid in the central clearing of over-the-counter (OTC) derivatives.

Classification of Risk Analytics Market

Risk analytics market is divided based on:

• Component type: Component segment of risk analytics market is further classified based on:
• Type of solution: Risk analytics software group for regulatory compliance, market risk management, credit risk management, etc. are included in this group.
• Services: Software services associated with risk analytics software like systems integration and risk evaluation are included in this group.
• Size of enterprise: Risk analytics market based on size of enterprise is further categorized as:
• Large organizations
• Small and medium organizations
• End-use verticals: Risk analytics market based on end-use vertical is further classified as:
• BFSI- Banking, financial services and insurance
• Manufacturing and retail
• Telecom and IT
• Government
• Energy and utility, etc.

Risk analytics is expected to draw large revenues from BFSI sector. Recent times have seen developing countries perform better than the developed economies. This causes currency fluctuations and entails considerable risk. In the face of this current economic climate, BSFI sector is demanding improved risk analytics solution. State-of-art risk analytics tools are an absolute necessity for BSFI sector as they have to spot potential frauds using statistical models.

Main Drivers of Risk Analytics Market

1. Market risk augmentation owing to:
• Lack of economic stability
• Market competitiveness
1. Stringent regulations and policies are causing a surge in the demand of risk analytics software. Following are some policies responsible for the increased demand:
• Basel I and II
• Comprehensive Capital Analysis and Review
• Dodd-Frank Wall Street Reform
• Consumer Protection Act (CCAR/DFAST)

Small and medium sized enterprises lack cognizance of risk analytics tools. Moreover, a hefty amount of money is required for the installation of risk analytics tools. These issues are likely to hinder the growth of risk analytics market.

A developed IT sector and authoritative presence of blue chip companies are the key factors boosting the development of risk analytics market. North America is expected to hold majority of the market share in risk analytics market. Significant growth in risk analytics market is likely to occur in the Asia Pacific region. The growing competition in the market and fluctuations in currency will fuel the demand of risk analytic tools.

Major Vendors in Risk Analytics Market

• IBM Corporation
• SAP SE
• Tata Consultancy Services Ltd.
• SAS Institute
• Oracle Corporation, etc.

With the rise in global risk, companies have to adopt new approaches to analyze risk. Big data and artificial intelligence are paving the way for the development of revolutionary strategies. CEOs are seeking the valuable input of insurers to curb the threat of cybercrime. Risk teams are turning into strategic advisors.

To know more about risk analytics follow Dexlab Analytics- a premium analytics training institute in Delhi. To gain proficiency in credit management tool, enroll for their credit risk modeling courses.

## How Credit Unions Can Capitalize on Data through Enterprise Integration of Data Analytics

To get valuable insights from the enormous quantity of data generated, credit unions need to move towards enterprise integration of data. This is a company-wide data democratization process that helps all departments within the credit union to manage and analyze their data. It allows each team member easy-access and proper utilization of relevant data.

However, awareness about the advantages of enterprise-wide data analytics isn’t sufficient for credit unions to deploy this system. Here is a three step guide to help credit unions get smarter in data handling.

### Improve the quality of data

A robust and functional customer data set is of foremost importance. Unorganized data will hinder forming correct opinions about customer behavior. The following steps will ensure that relevant data enters the business analytics tools.

• Integration of various analytics activity- Instead of operating separate analytics software for digital marketing, credit risk analytics, fraud detection and other financial activities, it is better to have a centralized system which integrates these activities. It is helpful for gathering cross-operational cognizance.
• Experienced analytics vendors should be chosen- Vendors with experience can access a wide range of data. Hence, they can deliver information that is more valuable. They also provide pre-existing integrations.
• Consider unconventional sources of data- Unstructured data from unconventional sources like social media and third-parties should be valued as it will prove useful in the future.
• Continuous data cleansing that evolves with time- Clean data is essential for providing correct data. The data should be organized, error-free and formatted.

### Data structure customized for credit unions

The business analytics tools for credit unions should perform the following analyses:

• Analyzing the growth and fall in customers depending on their age, location, branch, products used, etc.
• Measure the profit through the count of balances
• Analyze the Performances of the staffs and members in a particular department or branch
• Sales ratios reporting
• Age distribution of account holders in a particular geographic location.
• Perform trend analysis as and when required
• Analyze satisfaction levels of members
• Keep track of the transactions performed by members
• Track the inquires made at call centers and online banking portals
• Analyze the behavior of self-serve vs. non-self serve users based on different demographics
• Determine the different types of accounts being opened and figure out the source responsible for the highest transactions.

### User-friendly interfaces for manipulating data

Important decisions like growing revenue, mitigating risks and improving customer experience should be based on insights drawn using analytics tools. Hence, accessing the data should be a simple process. These following user-interface features will help make data user-friendly.

Dashboards- Dashboards makes data comprehensible even for non-techies as it makes data visually-pleasing. It provides at-a glance view of the key metrics, like lead generation rates and profitability sliced using demographics. Different datasets can be viewed in one place.

Scorecards- A scorecard is a type of report that compares a person’s performance against his goals. It measures success based on Key Performance Indicators (KPIs) and aids in keeping members accountable.

Automated reports- Primary stakeholders should be provided automated reports via mails on a daily basis so that they have access to all the relevant information.

Data analytics should encompass all departments of a credit union. This will help drawing better insights and improve KPI tracking. Thus, the overall performance of the credit union will become better and more efficient with time.

Technologies that help organizations draw valuable insights from their data are becoming very popular. To know more about these technologies follow Dexlab Analytics- a premier institute providing business analyst training courses in Gurgaon and do take a look at their credit risk modeling training course.

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

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