New Data Sources Emerging in Financial Risk Management
NEW DATA SOURCES EMERGING IN
FINANCIAL RISK MANAGEMENT
NEW DATA SOURCES EMERGING IN
FINANCIAL RISK MANAGEMENT
NEW DATA SOURCES EMERGING IN
FINANCIAL RISK MANAGEMENT
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Whitepaper
Whitepaper
Whitepaper
Introduction
The big data revolution has moved beyond the early adopter stage in the financial services sector. With alternative lenders disrupting the marketplace and big banks using huge quantities of historic data to establish machine learning functionality, the rest of the industry is left figuring out how to use fintech innovation to keep up with the competition.
For regional banks and credit unions, the challenge becomes identifying how to introduce the fintech innovation that enables alternative lending into everyday operations. In most cases, this is accomplished by either adopting dedicated big data platforms or partnering with an alternative lender. However, this is just the first step.
Once the technology is in place, banks and credit unions must assess how to make sense of the new data they have at their fingertips. This process can be incredibly demanding and puts pressure on financial institutions to pin down which types of data are accessible to them and how they can make that information actionable.
To fully understand this situation, it is vital to consider how big data is impacting the financial services sector. THE GROWING ROLE OF BIG DATA IN FINANCIAL SERVICES
The Harvard Business Review surveyed executives in a variety of segments, including financial services, to find out what they think about big data. It found that 63 percent of companies had a big data program in place as of 2015. Another 63 percent said they plan to put at least $10 million into their big data systems in 2017.
A Technavio study reinforces the emphasis on big data, as it found that spending on analytics in the financial sector will rise at a compound annual growth rate of 26 percent for the period of 2015 through 2019. By 2019, the market for big data in financial sectors in the Americas will be valued at $6 billion.
Big data can’t be ignored if financial institutions want to keep up with competitors, and many analytics programs are centered on leveraging new data sources.
THE RELATIONSHIP BETWEEN ANALYTICS AND NEW INFORMATION SOURCES
In a research report titled, “The Practice of Credit Risk Modeling for Alternative Lending,” industry experts Keith Shields and Bruce Lund explored precisely how alternative lending organizations were able to use fintech innovation to disrupt the financial services marketplace.
The innovation came from a variety of sources, but it primarily boiled down to a key point - alternative lenders began using more varied types of data than traditional banks and credit unions.
A credit risk scorecard using traditional data sources will use a system of additive weights to analyze and a credit application, the report explained. In most cases, these methods are built around linear statistical models that use data sourced from highly structured historic records. Alternative lenders have turned this system on its edge. The financial crisis of 2008 led to increased regulatory scrutiny and more conservative policies from most banks and credit unions.
This forced those institutions to change how they interact with the market and close off riskier or less profitable lines of the business.
Alternative lending stepped in to fill that gap, and they did so through the analysis of collateral sources that most banks don’t account for and funding sources that don’t get much attention from typical financial institutions.
Alternative lenders took on heavy costs to seek out unconventional data sources, establishing highly digital processes and workflows that underpin credit risk analysis, the news source explained. Within this environment, these lenders began exploring such data types as:Financial data deep dives:
Solutions capable of pulling data out of .PDF files enable creditors to get access to complete bank statements and similar documents and analyze that information in real time. The report pointed out that this ability to analyze a wider range of banking history data makes lenders less reliant on self-reported income.
Unstructured data:
The news source pointed out that analytics platforms are being used to mine social media systems for indicators of financial details, such as a person complaining of being “broke” on Twitter. Even reviews of small businesses are being accounted for in identifying the risk associated with a loan request.
Predictive data:
Big data enables organizations to find the answers to unusual questions, such as “What is the relationship between driving record and financial risk?” Analytics platforms are gathering raw data about individuals from the growing range of available sources and using that information to identify any trends or tendencies that may be relevant to loan risk, the news source said.
The ability to access these information types can be invaluable for financial services firms hoping to keep pace with the increasingly competitive lending marketplace. As regional banks and credit unions consider the best way to mitigate risk while they explore new analytics models. AVOIDING RISK IN AN EXPERIMENTAL DIGITAL ENVIRONMENT Alternative lenders were able to get a hold in the industry because they could take on risk that typical banks can’t tolerate. Major banks have been able to move into analytics with less risk because they already manage large quantities of data and major IT systems. Regional banks and credit unions must be
incredibly careful to ensure they don’t take on excessive risk as they try to adapt around digital transformation. McKinsey explored
typical risk scenarios for financial services firms exploring a digital transformation, and found that the efficiency and productivity gains
offered by embracing digital can expect cost reductions of approximately 25 percent in their
credit processes.
The key, according to McKinsey, is to mitigate the potential risks of highly automated digital
environments built around analytics with transparency and excellent regulatory reporting.FINDING THE RIGHT DATA SOURCES FOR YOUR NEEDS
With everything from social media to unstructured financial records gaining momentum in the risk analysis sector, financial services firms must be careful not to get overwhelmed by the amount of information at their fingertips.
This is where robust analytics solutions providers, such as GDS Link, can step in. Our platform focuses on automatically gathering and analyzing data in the backend for you. Because of this, the data is delivered in clear, actionable ways, making it easy to identify which data is relevant for the audiences you are trying to reach, and which isn’t.
The big data revolution can seem overwhelming, but analytics solutions that deliver data in digestible ways empower institutions to choose which new information sources can be valuable for them.About GDS Link:
GDS Link is a global leader in credit risk management, providing tailored software solutions, analytical and consulting services. Our customer-centric risk management and process automation platforms are designed for the modern lender in their pursuit to capitalize on the entire credit lifecycle. By providing a personal, consultative approach and leveraging our own industry-leading knowledge and expertise, GDS Link’s solutions and services deliver exceptional value and proven results.
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