Online Lending With AI and Big Data Archives - DexLab Analytics | Big Data Hadoop SAS R Analytics Predictive Modeling & Excel VBA

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

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 6th 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 6th quarter.

To find the missing value of 6th 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 3rd 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= Ft= Ft-1 + (At-1 – Ft-1)

Now lets see a practical example.

For this example we will be taking  = 0.5

Taking the same data……

 QTR (quarter) Price(At) EMS Price(Ft) 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 A1. 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(At) EMS Price(Ft) 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 to Change the Game of Online Lending With AI and Big Data

As digitization grows in size and width, more and more companies are seeking ways to modify their digital lending services. It would be effective as well as profitable for both borrowers and lenders. And as a topping on the cake, companies resort to Artificial Intelligence and Big Data as they believe they are the future powerhouse of loans.

Originally banks being the lenders make the lending decision based on a loan applicant’s credit score – which is a 3-digit number collected from renowned credit bureaus, like Equifax and Experian. Credit scores are obtained from large piles of data, such as credit history length, payment history and credit line amounts, and are used to decide whether the applicants would be able to repay their debts. They are also good to determine the interest rate of loans.

Low credit score is an indication that you are a risky borrower, which may end up in rejection of your loan application or else you have to pay excessively higher interest rate.

DexLab Analytics excels in providing superior business analysis training in Gurgaon. Visit the site for more information.

Artificial Intelligence: What the Future Holds for India, Next to US – @Dexlabanalytics.

However, according to digital lending platforms, this kind of information isn’t enough – they fail to draw the actual picture of the loan applicant’s credit worthiness. Rather, it is advisable to include hundred other data points in the scrutiny process, and they don’t have to be based on financial interactions alone. Include educational certifications, employment documents, and even you can take help from minor information, like your nap time, website browsing preferences, chatting habits and so on.

The mechanism of Peer-To-Peer Lending

At times, the concept of Big Data is downright challenging – it creates more confusion than clearing things out. Even Artificial Intelligence is included in this, though marketing teams of countless companies are relying on this advanced technology to enhance profitability and efficiency in operations – pundits from the online lending industry believes AI can actually change the way fintech companies perform.

Leveraging AI

For example, Upstart, a California-based Peer-to-Peer online lending company uses the power of AI to process loans. It implements machine learning algorithms to perform underwriting decisions. Machine Learning possesses the ability to analyze and coordinate large chunks of customer data to draw patterns that would remain unnoticed if done manually through human analysts.

According to Upstart, this process eventually works out well for people with limited credit history, lower income level and young borrowers. The company has also initiated an automation of 25% of its less risky loans to keep future prospects in mind.

Another Chicago-based startup Avant is harnessing machine learning to identify fraud – by comparing customer behavior with the initial available data belonging to normal customers, while singling out outliers. They are now planning to extend their services to brick-and-mortar banking structures that are planning to set their foot in the online lending business.

5 Hottest Online Applications Inspired by Artificial Intelligence – @Dexlabanalytics.

Today, digital lending is witnessing a steady growth worldwide, and India is not lagging behind. The perks of introducing machine learning and analytics are evident everywhere, so get yourself charged up and ride on the road of analytics. DexLab Analytics offers excellent big data hadoop certification in delhi ncr. Get enrolled today to experience the best!!