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ARIMA (Auto-Regressive Integrated Moving Average)

This is another blog added to the series of time series forecasting. In this particular blog  I will be discussing about the basic concepts of ARIMA model.

So what is ARIMA?

ARIMA also known as Autoregressive Integrated Moving Average is a time series forecasting model that helps us predict the future values on the basis of the past values. This model predicts the future values on the basis of the data’s own lags and its lagged errors.

When a  data does not reflect any seasonal changes and plus it does not have a pattern of random white noise or residual then  an ARIMA model can be used for forecasting.

There are three parameters attributed to an ARIMA model p, q and d :-

p :- corresponds to the autoregressive part

q:- corresponds to the moving average part.

d:- corresponds to number of differencing required to make the data stationary.

In our previous blog we have already discussed in detail what is p and q but what we haven’t discussed is what is d and what is the meaning of differencing (a term missing in ARMA model).

Since AR is a linear regression model and works best when the independent variables are not correlated, differencing can be used to make the model stationary which is subtracting the previous value from the current value so that the prediction of any further values can be stabilized .  In case the model is already stationary the value of d=0. Therefore “differencing is the minimum number of deductions required to make the model stationary”. The order of d depends on exactly when your model becomes stationary i.e. in case  the autocorrelation is positive over 10 lags then we can do further differencing otherwise in case autocorrelation is very negative at the first lag then we have an over-differenced series.

The formula for the ARIMA model would be:-

To check if ARIMA model is suited for our dataset i.e. to check the stationary of the data we will apply Dickey Fuller test and depending on the results we will  using differencing.

In my next blog I will be discussing about how to perform time series forecasting using ARIMA model manually and what is Dickey Fuller test and how to apply that, so just keep on following us for more.

So, with that we come to the end of the discussion on the ARIMA Model. 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.

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ARMA- Time Series Analysis Part 4

ARMA(p,q) model in time series forecasting is a combination of Autoregressive  Process also known as AR Process and Moving Average (MA) Process where p corresponds to the autoregressive part and q corresponds to the moving average part.

Autoregressive Process (AR) :- When the value of Yt in a time series data is regressed over its own past value then it is called an autoregressive process where p is the order of lag into consideration.

Where,

Yt = observation which we need to find out.

α1= parameter of an autoregressive model

Yt-1= observation in the previous period

ut= error term

The equation above follows the first order of autoregressive process or AR(1) and the value of p is 1. Hence the value of Yt in the period ‘t’ depends upon its previous year value and a random term.

Moving Average (MA) Process :- When the value of Yt  of order q in a time series data depends on the weighted sum of current and the q recent errors i.e. a linear combination of error terms then it is called a moving average process which can be written as :-

yt = observation which we need to find out

α= constant term

βut-q= error over the period q .

ARMA (Autoregressive Moving Average) Process :-

The above equation shows that value of Y in time period ‘t’ can be derived by taking into consideration the order of lag p which in the above case is 1 i.e. previous year’s observation and the weighted average of the error term over a period of time q which in case of the above equation is 1.

How to decide the value of p and q?

Two of the most important methods to obtain the best possible values of p and q are ACF and PACF plots.

ACF (Auto-correlation function) :- This function calculates the auto-correlation of the complete data on the basis of lagged values which when plotted helps us choose the value of q that is to be considered to find the value of Yt. In simple words how many years residual can help us predict the value of Yt can obtained with the help of ACF, if the value of correlation is above a certain point then that amount of lagged values can be used to predict Yt.

Using the stock price of tesla between the years 2012 and 2017 we can use the .acf() method in python to obtain the value of p.

.DataReader() method is used to extract the data from web.

The above graph shows that beyond the lag 350 the correlation moved towards 0 and then negative.

PACF (Partial auto-correlation function) :- Pacf helps find the direct effect of the past lag by removing the residual effect of the lags in between. Pacf helps in obtaining the value of AR where as acf helps in obtaining the value of MA i.e. q. Both the methods together can be use find the optimum value of p and q in a time series data set.

Lets check out how to apply pacf in python.

As you can see in the above graph after the second lag the line moved within the confidence band therefore the value of p will be 2.

So, with that we come to the end of the discussion on the ARMA Model. 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.

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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.

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.

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The Growing Significance of AI in Credit Risk Management

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.

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DexLab Analytics Rated One of The Best Institutes in India

Analytics India Magazine (AIM), one of the foremost journals on big data and AI in India, has rated Dexlab Analytics’ credit risk modelling course one of the best in India and recommended it be taken up to learn the subject in 2020. Dexlab Analytics is on AIM’s list of nine best online courses on the subject.

In an article, the AIM has rated DexLab Analytics as a premier institute offering a robust course in credit risk modelling. Credit risk modelling is “the analysis of the credit risk that helps in understanding the uncertainty that a lender runs before lending money to borrowers”.

The article describes the Dexlab Analytics course as offering learners “an opportunity to understand the measure of central tendency theorem, measures of dispersion, probability theory and probability distribution, sampling techniques, estimation theory, types of statistical tests, linear regression, logistic regression. Besides, you will learn the application of machine learning algorithms such as Decision tree, Random Forest, XGBoost, Support Vector Machine, banking products and processes, uses of the scorecard, scorecard model development, use of scorecard for designing business strategies of a bank, LGD, PD, EAD, and much more.”

The other bodies offering competent courses on the subject on AIM’s list are Udemy, SAS, Redcliffe Training, EDUCBA, Moneyweb CPD HUB, 365 DataScience and DataCamp.

Analytics India Magazine chronicles technological progress in the space of analytics, artificial intelligence, data science & big data by highlighting the innovations, players, and challenges shaping the future of India through promotion and discussion of ideas and thoughts by smart, ardent, action-oriented individuals who want to change the world.

Since 2012, Analytics India Magazine has been dedicated to passionately championing and promoting the analytics ecosystem in India. We have been a pre-eminent source of news, information and analysis for the Indian analytics ecosystem, covering opinions, analysis, and insights on key breakthroughs and future trends in data-driven technologies as well as highlighting how they’re being leveraged for future impact.

Dexlab Analytics has been thriving as one of the prominent institutes offering the best selection of courses on Big Data Hadoop, R Programming, Python, Business Analytics, Data Science, Machine Learning, Deep Learning, Data Visualization using Tableau and Excel. Moreover, it aims to achieve Corporate Training Excellence with each training it conducts.

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Stay Home and Upskill to Beat the Impact of a Global Recession

The US economy, as it was officially announced by the United States National Bureau of Economic Research on June 8, entered a recession in February after hitting a peak of economic activity and growth. This is the first time the US economy has undergone a recession since the global financial crisis of 2008-09, says a report.

In the US alone, 19.6 lakh cases of covid-19 positive patients have been reported till date with 1.1 lakh cases of deaths recorded, the highest for any country in the world. In such a dire situation, the silver lining seems to be the fact that this recession, intensified by the lockdown that the country has imposed on itself to abate the spread of the disease, might be deep but short lived, The New York Times reported.

Irrespective of when the recession will end, poverty levels have already begun spiking the world over. The World Bank has said that, “the highest share of countries in 150 years would enter recessions at the same time. As many as 90% of the 183 economies () examined are expected to suffer from falling levels of gross domestic product (GDP) in 2020, even more than the 85% of nations suffering from recession during the Great Depression of the 1930s”, The Guardian reported.

This will lead to dramatic rise in levels of poverty the world over. However, India might fare better on the global front for more reasons than one. Some economists feel “the (Indian) economy may do better than some other developing economies, which are heavily dependent on world trade” because of “lower dependence on exports (that) means less exposure to the decline in world trade. This and the low price of crude oil, our biggest import, may mean that we don’t suffer an external shock”.

In such circumstances, it is advisable that you stay home and not despair. Doing nothing but fretting will only add to your woes and not help the situation. Neither will binge-watching web series help. Instead, what you can do is ready yourself for a post COVID-19 world. You can do this by primarily upskilling yourself i.e.upgrading your skill set.

The only way to do this is remotely, though online classes available by the dozen. In fact, celebrities like Shakira have begun taking online classes (she in ancient philosophy) this lockdown while others like director Kevin Smith have finished old pending projects. The best skills to upgrade would, however, be those pertaining to computer science courses like big data, machine learning, deep learning or even credit risk modelling. These high-in-demand courses will look good on your résumé and instantly add to your employability wherever you plan to move to next.

In India, DexLab Analytics, a premier institute offering some of the best credit risk modelling training courses and R programming courses in Gurgaon, suggests you try and learn a new programming language or enrol in a new business analytics course so your résumé stands stronger than it was before the lockdown. This will help you beat competition when you will be searching for work opportunities post the lockdown.

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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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