The post depression bedlam is clearing for developed nations. Nevertheless, the household debts are shooting up, making credit risk managers face growing default rates. As per the reports of International Finance, household debts have risen by USD 7.7 trillion since the year 2007 till 2015. At present, the debts stand at a whopping amount of USD 44 trillion – the figures can give anyone a nightmare!
In such a topsy-turvy situation, credit risk managers should look for ingenious methods to lower default rates and keep accuracy in check. Application of data analytics infused with Big Data can come to their rescue.
The term Big Data is really very big! Big Data can help draw crucial insights that would help financial institutions in analyzing their customer base and how their purchase decision patterns vary. It can also be used to enhance business results, especially in regard to credit risk management.
If you follow the current business news, in the coming three years, banks would be facing two major risks – Credit and Liquidity. However, if credit risk managers follow the below-mentioned ways, they can turn this complication into an opportunity:
- Data Analytics determines a person’s behavior and how his circumstances have changed. This is verified by his social media activity, which further affirms how his financial position has changed with time. Hence, the chances of fraud and non-repayment are put in check.
- With proper analysis of mobile and social media data, credit risk managers may be able to gather insights and broaden their market horizon, enhancing the market base.
- Data science can establish contact with low risk customers.
Parsing data with Python should always be discussed after getting a good grip on the nuances of machine learning because both the intricate concepts are interlaced with each other. Click on the link first pythonprogramming.net/downloads/intraQuarter.zip and then go forward with parsing the data.
The data set given in the above link resembles the data set we caught hold of when we first visited the webpages before. The point of interest here is that we don’t need to visit the page even. We just need to have the full HTML source code, that’s it! This system is quite similar to parsing the website without disturbing bandwidth use.