As the financial services industry continues to collect customer and market data at an exponential rate, it's becoming clear that banks have to find a way to make sense of all the new incoming information. Gartner predicted at the beginning of 2016 that by 2019, 90 percent of large organizations would create a C-suite position for a "chief data officer" role, citing the need to solve complex issues by using data and create an effective information strategy with relevant metrics.
"Business leaders are starting to grasp the huge potential of digital business, and demanding a better return on their organizations' information assets and use of analytics," said Mario Faria, research vice president at Gartner. "It's a logical step to create an executive position – the CDO – to handle the many opportunities and responsibilities that arise from industrial-scale collection and harnessing of data."
Why do banks need to consider risk-based pricing solutions in this new age of data analytics? Let's take a look:
"Improving loan pricing accuracy and decreasing default rates should both be key goals."
Credit risk continues to fluctuate
Risk management in banking is a delicate undertaking. Credit risk managers in countries around the world are dealing with the effects of global financial issues. Improving loan pricing accuracy and decreasing default rates should both be key goals. Compounding this credit risk issue is the fact that household debt continues to rise. At the end of 2015, the Institute of International Finance reported that global household debt had gone up by $7.7 trillion since 2007 to sit at more than $44 trillion – $6.2 trillion of which lay in emerging markets.
In order to account for household debt and survive in the volatile economic climate, banks need to consider using analytics tools to parse through all the data they've collected. By improving the accuracy with which loans are priced and credit is issued, organizations can curb risks down the line.
The big deal with big data
Big data isn't just information that sits on servers waiting to be accessed: It carries important information that, once decrypted, can be used to fill in the gaps in any loan pricing strategy. Therefore, banks need to find a way to drill down into this data and draw out key insights that can be used during the loan pricing process. ComputerWeekly contributor Paul Garel-Jones noted that banks can use analytics to unlock important pieces of information such as customer behavior.
"The opportunity for the sector is to unlock the potential in the data through analytics and shape the strategy for business through reliable factual insight rather than intuition," Garel-Jones wrote. "Unlocking the insights in the data to better understand customers, competitors and employees represents a significant opportunity to gain competitive advantage."
Analytics is giving banks the tools they need to create better portfolios and manage the risks associated with loan pricing. Therefore, risk-based pricing solutions like DealPoint can have a positive impact on the way banks create pricing models and serve their customers. However, there is plenty of room for improvement as far as the industry is concerned: According to a 2014 report published by the Economist Intelligence Unit, only 42 percent of banks had the ability to integrate, manipulate and query big data when creating risk profiles.
With the key insights provided by DealPoint software, banking professionals can make sure they're accurately pricing loans and managing credit risks effectively. DealPoint, which offers risk-based pricing and profitability measurement solutions, allows organizations to increase margins and more effectively manage risk by drilling down into and bringing out key data points from their customers' information.
For more information about DealPoint and about how analytics is the way for banks to accurately price loans, contact Brilliance Financial Technology today.