Better risk management through financial data analytics
Within the world of finance, data analytics itself is nothing new. After all, financial analysts are accustomed to collecting and analyzing large data sets about things like stock prices and using them to generate predictions and risk assessments. What makes current developments in financial data analytics so different from everything that has come before is the advent of big data. Financial institutions are now gaining access to large quantities of previously untapped data – such as consumer transaction data – that could prove invaluable for risk management.
More accurate credit risk profiling
Big data can be used to analyze customer purchasing behavior, which can then be used to create revenue forecasts for companies applying for credit. This makes it possible to assess the risks involved in granting credit to that company much more precisely. Financial data analytics yields more accurate risk profiles than ever before, thereby leading to improved risk assessment.
Getting the hang of big data
Though data analytics is still in its infancy, the financial sector has been investing heavily in it. Crunching numbers and running data is child’s play for modern financial institutions. But big data is a whole different ball game. In the coming years, institutions will have to figure out how best to make big data work for them. The aim is to improve financial analysis by using data analytics to discover trends and connections between different types of data, and then use this information to make accurate predictions. Of course, predictions are only as good as the data they’re based on, but that’s the nature of the game.
The importance of expertise in financial data analytics
For financial institutions seeking to invest in financial data analytics, it is crucial to hire professionals who are skilled at creating, curating, and analyzing data sets. Investing in the necessary knowledge and expertise will enable institutions to perform accurate analyses and draw the correct conclusions from correlations between data. In addition to attracting talent, institutions must also continue to invest in expanding their in-house expertise. There must be enough people on board who can speak knowledgeably about data analytics and use it to make strategic decisions. Data analytics isn’t confined to the realm of technology, but also has a role to play on the business side of things. Ultimately, data analytics can even lead to the development of new products and business models.
Tip: Invest wisely
Don’t limit yourself to investing in data sets and data analyses, but make sure to also invest in understanding the relationships between the data. Data analytics is not an end in itself, but rather a means to generate information with predictive value that can be of use to your organization.
Greatest challenges
When it comes to data analytics, the greatest challenges are extracting meaningful relationships from the data and understanding what the data tells you in order to make more confident predictions.
Ready to expand your knowledge of financial data analytics?
Our part-time Master in Finance <link> has been updated to include a module on Financial Data Analytics. This module covers the fundamentals of financial data analytics, how to identify suitable data, and the financial insights that can be revealed. Throughout the course, you’ll also receive the tools you need to process the data and perform relevant financial analyses. We’ll begin with the basics, such as how to use a company's balance sheets and profit and loss accounts from the previous five years to draw financial conclusions, before moving on to more sophisticated analyses of large data sets.
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