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Time Series Analysis & Modelling with Python (Part II) – Data Smoothing

Posted on January 20, 2021January 20, 2021 by Dexlab

Data Smoothing is done to better understand the hidden patterns in the data. In the non- stationary processes, it is very hard to forecast the data as the variance over a period of time changes, therefore data smoothing techniques are used to smooth out the irregular roughness to see a clearer signal.

In this segment we will be discussing two of the most important data smoothing techniques :-

Moving average smoothing
Exponential smoothing

Moving average smoothing

Moving average is a technique where subsets of original data are created and then average of each subset is taken to smooth out the data and find the value in between each subset which better helps to see the trend over a period of time.

Lets take an example to better understand the problem.

Suppose that we have a data of price observed over a period of time and it is a non-stationary data so that the tend is hard to recognize.

QTR (quarter)	Price
1	10
2	11
3	18
4	14
5	15
6	?

In the above data we don’t know the value of the 6^th quarter.

….fig (1)

The plot above shows that there is no trend the data is following so to better understand the pattern we calculate the moving average over three quarter at a time so that we get in between values as well as we get the missing value of the 6^th quarter.

To find the missing value of 6^th quarter we will use previous three quarter’s data i.e.

MAS = = 15.7

QTR (quarter)	Price
1	10
2	11
3	18
4	14
5	15
6	15.7

MAS = = 13

MAS = = 14.33

QTR (quarter)	Price	MAS (Price)
1	10	10
2	11	11
3	18	18
4	14	13
5	15	14.33
6	15.7	15.7

….. fig (2)

In the above graph we can see that after 3^rd quarter there is an upward sloping trend in the data.

Exponential Data Smoothing

In this method a larger weight ( ) which lies between 0 & 1 is given to the most recent observations and as the observation grows more distant the weight decreases exponentially.

The weights are decided on the basis how the data is, in case the data has low movement then we will choose the value of closer to 0 and in case the data has a lot more randomness then in that case we would like to choose the value of closer to 1.

EMA= F_t= F_t-1 + (A_t-1 – F_t-1)

Now lets see a practical example.

For this example we will be taking = 0.5

Taking the same data……

QTR (quarter)	Price (A_t)	EMS Price(F_t)
1	10	10
2	11	?
3	18	?
4	14	?
5	15	?
6	?	?

To find the value of yellow cell we need to find out the value of all the blue cells and since we do not have the initial value of F₁ we will use the value of A_1.Now lets do the calculation:-

F₂=10+0.5(10 – 10) = 10

F₃=10+0.5(11 – 10) = 10.5

F₄=10.5+0.5(18 – 10.5) = 14.25

F₅=14.25+0.5(14 – 14.25) = 14.13

F₆=14.13+0.5(15 – 14.13)= 14.56

QTR (quarter)	Price (A_t)	EMS Price(F_t)
1	10	10
2	11	10
3	18	10.5
4	14	14.25
5	15	14.13
6	14.56	14.56

In the above graph we see that there is a trend now where the data is moving in the upward direction.

So, with that we come to the end of the discussion on the Data smoothing method. Hopefully it helped you understand the topic, for more information you can also watch the video tutorial attached down this blog. The blog is designed and prepared by Niharika Rai, Analytics Consultant, DexLab Analytics DexLab Analytics offers machine learning courses in Gurgaon. To keep on learning more, follow DexLab Analytics blog.

Time Series Analysis Part I

Posted on January 18, 2021January 18, 2021 by Dexlab

A time series is a sequence of numerical data in which each item is associated with a particular instant in time. Many sets of data appear as time series: a monthly sequence of the quantity of goods shipped from a factory, a weekly series of the number of road accidents, daily rainfall amounts, hourly observations made on the yield of a chemical process, and so on. Examples of time series abound in such fields as economics, business, engineering, the natural sciences (especially geophysics and meteorology), and the social sciences.

Univariate time series analysis- When we have a single sequence of data observed over time then it is called univariate time series analysis.
Multivariate time series analysis – When we have several sets of data for the same sequence of time periods to observe then it is called multivariate time series analysis.

The data used in time series analysis is a random variable (Yt) where t is denoted as time and such a collection of random variables ordered in time is called random or stochastic process.

Stationary: A time series is said to be stationary when all the moments of its probability distribution i.e. mean, variance , covariance etc. are invariant over time. It becomes quite easy forecast data in this kind of situation as the hidden patterns are recognizable which make predictions easy.

Non-stationary: A non-stationary time series will have a time varying mean or time varying variance or both, which makes it impossible to generalize the time series over other time periods.

Non stationary processes can further be explained with the help of a term called Random walk models. This term or theory usually is used in stock market which assumes that stock prices are independent of each other over time. Now there are two types of random walks:
Random walk with drift : When the observation that is to be predicted at a time ‘t’ is equal to last period’s value plus a constant or a drift (α) and the residual term (ε). It can be written as
Yt= α + Yt-1 + εt
The equation shows that Yt drifts upwards or downwards depending upon α being positive or negative and the mean and the variance also increases over time.
Random walk without drift: The random walk without a drift model observes that the values to be predicted at time ‘t’ is equal to last past period’s value plus a random shock.
Yt= Yt-1 + εt
Consider that the effect in one unit shock then the process started at some time 0 with a value of Y0
When t=1
Y1= Y0 + ε1
When t=2
Y2= Y1+ ε2= Y0 + ε1+ ε2
In general,
Yt= Y0+∑ εt
In this case as t increases the variance increases indefinitely whereas the mean value of Y is equal to its initial or starting value. Therefore the random walk model without drift is a non-stationary process.

So, with that we come to the end of the discussion on the Time Series. Hopefully it helped you understand time Series, for more information you can also watch the video tutorial attached down this blog. DexLab Analytics offers machine learning courses in delhi. To keep on learning more, follow DexLab Analytics blog.

How AI is Reshaping The Finance Industry?

Posted on August 13, 2020August 13, 2020 by Dexlab

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.

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.

The Growing Significance of AI in Credit Risk Management

Posted on July 13, 2020July 13, 2020 by Dexlab

With more and more sectors turning to AI to find real-time solutions, it is no wonder that AI would gain momentum in the field of credit risk management as well. AI adds efficiency to the process by offering an insight into the portfolios of potential borrowers, which was not available to financial firms up until now. This trend is pushing corporate houses to sign up for credit risk analytics training.

Let’s have a look at credit risk management and figure out how AI can play a key role in building the perfect model.

What is credit risk management

Banks and financial firms lend money to individuals as well as businesses, now credit risk refers to the uncertainty arising due to that borrower, delaying or failing to pay the amount borrowed along with interest resulting in the bank losing money. Remember that infamous recession of 2008?

Credit risk management is about mitigating the risk factor in the process. It involves identifying, analyzing, and measuring the risk factors to keep the risk at a minimal level, or, eliminating the risk if possible.

How AI features in credit risk management

In order to eliminate risk, the bank needs to identify the risk factors, which means going through data, mainly regarding the borrower’s financial activities, portfolio to decide whether that particular individual or, a commercial firm would be able to pay the money back before lending can happen.

But, the process needs to be as much error-free as possible, because while analyzing the portfolios any mistake could lead to failure to recognize a potential defaulter, or, might result in rejecting an applicant who could have been a valuable customer in future. Credit Risk Modelling Courses are being developed to train professionals to deal with this highly specialized task.

AI and especially its subset Machine Learning come into this picture, due to the massive amount of structured and unstructured data involved in the process. The traditional methodology applied by financial institutes is not error-free. processing a huge amount of raw data and identifying patterns is a job that is better handled by AI.

The benefits AI bring to the table

Despite financial firms implementing all sorts of solutions available to them, achieving efficiency in credit risk management has remained a challenge for them due to not having access to smart risk assessment tools, fault in the data management procedure. This is primarily the reason why AI is now being incorporated in the process to achieve better results. With Artificial Neural Networks, Random Forest in place, sorting through loan applications and portfolios to process valuable data and finding patterns becomes easier. Undergoing credit risk modelling certification is almost mandatory for any individual looking forward to having a career in this field. Here are the benefits AI has to offer

Data quality: When traditional models are employed they fail to deal with the issue of data quality which for any financial institute could be a big problem. But with Machine learning detecting any oddity in the data entry is easy. Another fact is that detecting complex patterns from diverse data sets to analyze risk factors is essential which traditional models are not equipped to perform.
Segmentation is better: While analyzing customer portfolios, AI could help in introducing smart segmentation solutions to gain a deep insight into their profiles, thus ensuring efficient risk recognition.
Automated process: Usually organizations have to put together a team for dealing with data handling and report generation tasks, but AI can automate the whole process and minimize the chances of human error while allowing the organizations to set people free to deal with other vital work. Automation would also lead to faster loan processing.
Intuitive analysis guarantees accuracy: Usually, traditional models are somewhat rigid, due to functioning as per set guidelines. However, with AI the analysis gets intuitive and as it continues to wade through new data sets, it continues to learn and come up with more accurate predictions.

Credit risk management will continue to be a key area for financial firms in the future as well, given the present circumstances, the risk factor would only grow. So, it is time for this sector to recognize and embrace the potential of AI in mitigating the risk.

A Beginner’s Guide to Credit Risk Modelling

Posted on April 30, 2020July 2, 2026 by Dexlab

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.

Character

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

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.”

Capital

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

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.”

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.

8 Applications of AI and Machine Learning in our Daily Lives

Posted on March 16, 2020July 2, 2026 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.

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 November 13, 2019July 2, 2026 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:

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.
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.
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.

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

Posted on May 28, 2018May 28, 2018 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.

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.

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

Posted on May 22, 2018May 22, 2018 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.

“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

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