While financial services organizations have been early adopters
of data analytics, they also have unique challenges. The need for compliance in
an industry filled with regulation, it’s critical for financial services organizations
to possess accurate and timely data. A mistake or decision made on inaccurate
data can cost financial services organizations time, money, and their
reputation. This post will explore various unique situations and challenges to
financial services organizations with data analytics.
Chief Data Officer
Trends
Banking and insurance are among the first industries to take
on the Chief Data Officer (CDO) role. Like government, these industries have
overarching regulatory requirements, risk-management practices, and a practical
knowledge of the importance of data in business and reporting to industry
regulators. CDOs exist in a world overcome by data, along with regulation and
risk, likely functioning in a complex organization. While the role of CDO
continues to evolve, there is an ongoing battle between a CDO’s alignment with
IT versus business. Since the use and
management of data belong to both business and IT, CDOs should make note that
one is not exclusive of the other. It’s truly a team effort.
Auditing
Internal auditors at financial services organizations also
benefit from data analytics. Business processes that pose financial,
regulatory, contractual, and fraud-related risks are often audited, as a result
auditors must be prepared to provide assurance within a dedicated timeframe. While
there are dedicated tools for data analysis for auditors, there are a few
capabilities that transcend tools and technologies:
- ETL (Extract, Transform, Load) – In order to obtain data from various (or even single) data sources, ETL is a set of functions that obtain data from sources such as an ERP, CRMs, and databases such as SQL Server.
- Data quality ensures the dataset is valid and complete, does not contain inconsistencies, and matches the data dictionary provided by the owner.
- Sampling a set of features that allow for selecting subsets of data based on questions that need to be answered (eg. How many insurance claims were created last month?)
- Query-based analysis via data sketch, histogram, and Benford diagram
- Lastly, reporting and workflow that supports auditors the ability to make decisions and draw conclusions. Traditional reporting, dashboards, and audit trails fall into this area.
Big Data
Financial services organizations have also been early
adopters of big data. Big data within financial organizations is also not
immune to regulation, security, and privacy, which all continue to be priorities.
In some countries, information barriers are required between certain types of
businesses in banking and insurance. With big data, organizations need to be
aware of deterministic versus probabilistic modeling. For instance, in
probabilistic model analysts should be aware of false positives, such as a
financial transaction following a certain path that money launderers follow. While
the transaction might follow a certain pattern, it might be for a different
reason.
Privacy concerns around big data have also been under the
microscope in the media in recent months. The credit and insurance
organizations possess volumes of data but need to continue compliance with the
Fair Credit Reporting Act, where these organizations do have permission to
collect personal data. Financial services organizations must continue to be
sensitive to these regulations as they move forward with big data initiatives. A
recent World Economic Forum reports shows promise in possible future standards,
where codes are assigned to an individual’s preference regarding how his/her
data will be used.
Disruption within the
Financial Industry
Speaking about personal data, there is discussion regarding the financial industry using personal data for new sources of revenue. Personal data bank (PDBs) is a concept where an entity will collect, protect, and monetize personal data and ensure that this data is entrusted to individuals. In theory, PDBs will operate with personal data similar to bank operations with customer money. According to Maverick Research by Gartner, PDBs can potentially create a new market and with the absence of government regulation, first mover advantage can take hold and as a result, have PayPal-like success.
References:
Big Data Is Opening
Doors, but Maybe Too Many. New York Times, March 2013: http://www.nytimes.com/2013/03/24/technology/big-data-and-a-renewed-debate-over-privacy.html
Big Data Analytics
Requires An Ethical Code of Conduct. Gartner ID G00256399. November 2013
CIO Advisory: The
Chief Data Officer Trend Gains Momentum. Gartner ID G00254672. January 2014
Maverick Research: Put
Your Data in the Bank, Get Dividends. Gartner ID G00264262. September 2014
Technology Overview:
Data Analysis for Auditors Can Lower Audit Costs and Detect Fraud. Gartner
ID G00247280. April 2013
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