Credit Risk Modelling Using SAS Archives - Page 2 of 4 - DexLab Analytics | Credit Risk | Market Risk | SAS Python Machine Learning Modeling

Category: Credit Risk Modelling Using SAS

How AI is Reshaping The Finance Industry?

Posted on by Dexlab

How AI Is Reshaping The Finance Industry?

Technology is bringing about rapid changes in almost every field it touches. Traditional finance tools no longer suit the current tech-friendly generation of investors who are now used to getting information, service at their fingertips. Unless the gap is bridged, it would be hard for firms to retain any clients. Some of the financial firms have already started investing in AI technology to develop a business model that satisfies the changing requirements of the customers and leverages their business.

The adoption of AI has finally enabled the firms to have access to customer-centric information to develop a plan that suits their individual financial goals and offer customer-centric solutions to offer a personalized experience.

AI is impacting the financial industry in more ways than one. Let’s take a look

Mitigating risks

The application of AI has enabled institutes to assess risk factors and mitigate risk. Implementation of AI tools allows the processing of a huge amount of financial records that comprise structured as well as unstructured data to recognize patterns and predict the risk factors. So, while approving a loan, for example, an institute could be better prepared as it would be able to identify those customers who are likely to default and having personnel with a background in credit risk management courses can certainly be of immense help here.

Detecting fraud

One of the most niggling issues faced by the banking institutes is a fraud, and with AI application being available fraud identification gets easier. When any such case happens it becomes almost impossible for institutes to recover the money. Along with that the banks especially also have to deal with false positives cases that can harm their business. Credit card fraud cases also have become rampant and give customers and banks sleepless nights. AI technology could be a great weapon in fighting and preventing such cases. By analyzing data regarding the transaction of a customer, his behavior, spending habits, past cases if any, an oddity could be easily spotted and an alarm could be sent to monitor the situation and take measures accordingly.

Trading gets easier

Investment always comes with a set of risks, the changing market scenario could certainly put your money in a volatile situation. However, with AI in place, the large datasets could be easily handled, and detecting market situations can help to make investors aware of the trends and they can change their investment decision accordingly. Faster data processing leads to quick decision making and coupled with an accurate prediction of the market situation, trading gets smarter as an investor can buy or, sell stock as per stock trends and stay risk-free.

Personalized banking experience

The integration of AI can offer customers a personalized financial experience. The chatbots are there to help the customers manage their affairs without needing any intervention. Be it checking balance or, scheduling payments everything is streamlined. In addition to this, the customers now have access to apps that help keep their financial transactions in check, track their investments, and plan finances without any hassle. There have been a dynamic progress in the field of NLP and the chatbots being developed now are getting smarter than ever and pursuing a natural language processing course in gurgaon, could lead to lucrative job opportunities.

 Process Automation

Every financial institution needs to run operations with maximum efficiency while adopting cost-cutting measures. The adoption of RPA has significantly changed the way these institutes function. Manual tasks which require time and labor could easily be automated and there would be fewer chances of error. Be it data verification or, report generation every single task could be well taken care of.

Data Science Machine Learning Certification

Examples of AI implementation in finance

  • Zest Automated Machine Learning (ZAML) is a platform that offers underwriting solutions. Borrowers with little or, no past credit history could be assessed.
  • Kensho combines the power of NLP and cloud computing to offer analytical solutions
  • Ayasdi provides anti-money laundering (AML) detection solutions to financial institutes
  • Abe AI is a virtual assistant that helps users with budgeting and saving while allowing them to track spending.
  • Darktrace offers cyber security solutions to financial firms

The powerful ways AI is helping the financial institutes excel in their field indicate a promising future ahead. However, the integration is slowly taking place, and still, there is some uncertainty regarding the technology. With proper training from an analytics lab could help bridge the knowledge gap and thus ensure full integration of this dynamic technology.


.

8 Applications of AI and Machine Learning in our Daily Lives

Posted on by Dexlab

8 Applications of AI and Machine Learning in our Daily Lives

Artificial intelligence (AI) and machine learning are today thought to be one of the biggest innovations since the microchip. With the advancement of the science of neural networks, scientists are making extraordinary breakthroughs in machine learning through what is termed as deep learning. These sciences are making life easier and more streamlined for us in more ways than one. Here are a few examples.

1. Smart Gaming

 

Artificial Intelligence and Machine Learning are used in smart gaming techniques, especially in games that primarily require the use of mental abilities like chess. Google DeepMind’s AlphaGo learnt to play chess, and defeat champions like Lee Sedol (in 2016) by not only studying the moves of masters but by learning how to play the game by practising against itself innumerable times.

2. Automated Transportation

When we fly in an airplane, we experience automated transportation in the sense that a human pilot is only flying the plane for a couple of minutes during take-off and landing. The rest of the flight is maneuvered by a Flight Management System, a synchronization of GPS, motion sensors and computer systems that track flight position. Google Maps has already revolutionized local transport by studying coordinates from smart phones to determine how fast or slow a vehicle is moving and therefore how much traffic there is on a given road at any point of time.

3. Dangerous Jobs

AI technology powered robots are taking over dangerous jobs like bomb disposal and welding. In bomb disposal, today, robots need to be controlled by humans. But scientists believe there will soon come a time when these tasks would be completed by robots themselves. This technology has already saved hundreds of lives. In the field of welding, a hazardous job which entails working in high levels of noise and heat in a toxic environment, robots are helping weld with greater accuracy.

Data Science Machine Learning Certification

4. Environmental Protection

Machine Learning and artificial intelligence run on big data, large caches of data and mind boggling statistics generated by computer systems. When put to use in the field of environmental protection, these technologies could be used to extract actionable solutions to untenable problems like environmental degradation. For instance, “IBM’s Green Horizon Project studies and analyzes environmental data from thousands of sensors and sources to produce accurate, evolving weather and pollution forecasts.”

5. Robots as Friends

A company in Japan has invented what it calls a robot companion named Pepper who can understand and feel emotions and empathy. Introduced in 2014, Pepper went on sale in 2015 and all the 1000 units were sold off immediately. “The robot was programmed to read human emotions, develop its own, and help its human friends stay happy,” a report says. Robots could also assist the aged in becoming independent and take care of themselves, says a computer scientist at Washington State University.

6. Health Care

Hospitals across the world are mulling over the adoption of AI and ML to treat patients so there are reduced instances of hospital related accidents and spread of diseases like sepsis. AI’s predictive models are helping in the fight against genetic diseases and heart ailments. Also, Deep Learning models which “quickly provide real-time insights and…are helping healthcare professionals diagnose patients faster and more accurately, develop innovative new drugs and treatments, reduce medical and diagnostic errors, predict adverse reactions, and lower the costs of healthcare for providers and patients.”

7. Digital Media

Machine learning has revolutionized the entertainment industry and technology has already found buyers in streaming services such as Netflix, Amazon Prime, Spotify, and Google Play. “ML algorithms are…making use of the almost endless stream of data about consumers’ viewing habits, helping streaming services offer more useful recommendations.”

These technologies will assist with the production of media too. NLP (Natural Language Processing) algorithms help write and compose trending news stories, thus cutting on production time. Moreover, a new MIT-developed AI model named Shelley “helps users write horror stories through deep learning algorithms and a bank of user-generated fiction.”

8. Home Security and Smart Stores

AI-integrated cameras and alarm systems are taking the home security world by storm. The cutting-edge systems “use facial recognition software and machine learning to build a catalog of your home’s frequent visitors, allowing these systems to detect uninvited guests in an instant.” Brick and Mortar stores are likely to adopt facial recognition for payments by shoppers. Biometric capabilities are largely being adopted to enhance the shopping experience.

Key Takeaway

AI is no longer the domain of fiction. It’s our new reality and is it no surprise then that it is revolutionizing our lives. Deep learning training institutes and Machine Learning courses in India along with Artificial Intelligence courses in Delhi abound because India too is attempting to make the most of the AI revolution.


.

Credit Risk Modeling: A Comprehensive Guide

Posted on by Dexlab

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.

Data Science Machine Learning Certification

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.

 


.

How Machine Learning Technology is Enhancing Credit Risk Modeling

Posted on by Dexlab

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.

Data Science Machine Learning Certification

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.


.

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

Posted on by Dexlab

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.

2

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.

 

Interested in a career in Data Analyst?

To learn more about Machine Learning Using Python and Spark – click here.
To learn more about Data Analyst with Advanced excel course – click here.
To learn more about Data Analyst with SAS Course – click here.
To learn more about Data Analyst with R Course – click here.
To learn more about Big Data Course – click here.

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

Posted on by Dexlab

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.

2

“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.

 

This article first appeared in – https://guardian.ng/business-services/technology-crucial-to-tackling-risks-skill-gaps-in-banks

 

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.

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

Posted on by Dexlab

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.

Let’s Take Your Data Dreams to the Next Level

  • 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.

 

Interested in a career in Data Analyst?

To learn more about Machine Learning Using Python and Spark – click here.

To learn more about Data Analyst with Advanced excel course – click here.
To learn more about Data Analyst with SAS Course – click here.
To learn more about Data Analyst with R Course – click here.
To learn more about Big Data Course – click here.

Predictive Analytics: What It is and Why It’s Important for Businesses

Posted on by Dexlab

Predictive Analytics: What It is and Why It’s Important for Businesses

Did you know that 2.5 quintillion bytes of data are generated on a daily basis? Big data is a valuable asset for companies provided that this data can be utilized to improve their performance. Companies employ predictive analytics to uncover hidden patterns in data and develop quick and efficient strategies that will steer their businesses forward.

IMB Watson is a popular predictive analytics processor that employs natural language processing technology to analyze human speech. IBM Watson can analyze a vast amount of data, often in a fraction of a second, to answer human-framed questions.

What is predictive analytics?

Predictive analytics use a combination of statistical modeling and machine learning techniques to determine the likelihood of future events based on historical data, which can come from structured, unstructured and semi-structured sources. A good example of the use of predictive analytics is the preparation of a credit report of a customer by a financial institution.

large

Credit Score:

Financial lenders use predictive analytics to scrutinize relevant data of an individual who has applied for a loan, including data pertaining to the individual’s current assets and debts, his/her employment and history of paying off loans. All this data is analyzed and boiled down to a single value known as credit score. This value represents the lending risk and helps the lender determine a customer’s creditworthiness. The higher the credit score, the more confident is the lender that the customer will fulfill his/her credit obligation.

predictive_analytics_and_cross-selling-01_1

Predictive analytics help lenders make quick and efficient decisions, such as accepting or rejecting a customer and increasing or decreasing their loan value. Credit risk modeling training has become extremely important across many sectors, including banking, insurance and retail.

Importance of predictive analytics:

Thanks to the plethora or new age analytics tools and software, predictive analytics make it easier for organizations to plan the future and gain competitive advantage.

Below are some ways in which predictive analytics are used:

  • To predict the probability of certain diseases affecting a specific group of people so that the necessary preventive healthcare measures can be taken.
  • To predict the probability of certain machine parts failing so that preventive maintenance can be administered.
  • To predict the probability of an interruption in a business’s supply chain.
  • To predict customer behavior.
  • To predict safety risks on railroads.
  • To predict traffic flows and the infrastructure requirements of a city.

3-3infographic-pa-12345

How businesses use predictive analytics:

It is imperative for every company to include predictive analytics in their technology portfolio. The major vendors of predictive analytics include SAP, IBM, Oracle, SAS, Information Builders, etc. Their on-premise and cloud-based versions give companies a lot of options to choose their predictive analytics tools from.

On-premise predictive analytics systems are used by companies requiring high level of analytical power and predictive intelligence. These include companies in the drug and pharmaceutical sector; companies working on life science fields like genomics; and research institutes and universities.

Cloud-based versions provide predictive analytics solutions to companies on a per usage or subscription basis. These are highly beneficial for small and medium sized companies where predictive analytics aren’t the core component, but they are still critical for their success and need to be fitted in a stipulated IT budget. Companies can use the ‘’try and buy’’ facility provided by cloud-vendors to test if a particular software is working for them before finalizing a contract.

Companies that lack prior experience in predictive analytics can opt for SaaS (Software as a Service), which are cloud-based solutions with expertise in a specific sector, for example healthcare.

Role of Business Leaders:

Business leaders must be skilled in using the insights provided by predictive analytics to develop strategies that drive their businesses forward. This includes two things; firstly coming up with well-construed questions and secondly identifying the right kind of data to analyze. These will determine whether predictive analytics is working for a company or not.

Companies in all industry verticals are employing predictive analytics to formulate future strategies. As mentioned in a report- ‘’the global market for predictive analytics is projected to grow to $3.6 billion USD by 2020.”

To more about predictive analytics follow Dexlab Analytics– a premier analytics training institute in Gurgaon. Do take a look at their credit risk modeling courses.

 

Interested in a career in Data Analyst?

To learn more about Machine Learning Using Python and Spark – click here.

To learn more about Data Analyst with Advanced excel course – click here.
To learn more about Data Analyst with SAS Course – click here.
To learn more about Data Analyst with R Course – click here.
To learn more about Big Data Course – click here.

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

Posted on by Dexlab

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.

Interested in a career in Data Analyst?

To learn more about Machine Learning Using Python and Spark – click here.
To learn more about Data Analyst with Advanced excel course – click here.
To learn more about Data Analyst with SAS Course – click here.
To learn more about Data Analyst with R Course – click here.
To learn more about Big Data Course – click here.

Call us to know more

Gurgaon

+91 8676079880

Kolkata

+91 9903662244

Quick Links

Our Courses

Important dates

Copyright © 2026 DexLab Solutions Corporation | Privacy Policy | Legal Disclaimer | Terms and Conditions