This is a sponsored blog post by Randy Koch, CEO of ARM Insight, a financial data technology company based in Portland, Oregon. Here, he explores what synthetic data is, and why financial institutions should start taking note.
You’ve heard it before – data is invaluable. The more data your company possesses the more innovation and insights you can bring to your customers, partners and solutions. But financial services organizations, which handle extremely sensitive card data and personally identifiable information (PII), face a difficult data management challenge. These organizations have to navigate how to use their data as an asset to increase efficiencies or reduce operational costs, all while maintaining privacy and security protocols necessary to comply with stringent industry regulations like the Payment Card Industry Data Security Standard (PCI DSS) and the General Data Protection Regulation (GDPR).
It’s a tall order.
We’ve found that by accurately finding and
converting sensitive data into a revolutionary new category – synthetic data –
financial services organizations can finally use sensitive data to maximize
business and cutting-edge technologies, like artificial intelligence and
machine learning solutions, without having to worry about compliance, security
and privacy.
But first, let’s examine the traditional types of data categorizations and dissect why financial services organizations shouldn’t rely on them to make data safe and usable.
Raw and Anonymous Data – High Security and Privacy Risk
The two most traditional types of data
categorization types – raw and anonymous – come with pros and cons. With raw
data, all the personally identifiable information (PII) fields for both the
consumer (name, social security number, email, phone, etc.) and the associated
transaction remain tagged to data. Raw data carries a considerable risk – and
institutional regulations and customer terms and conditions mandate strict
privacy standards for raw data management. If a hacker or an insider threat were
to exfiltrate this type of data, the compliance violations and breach headlines
would be dire. To use raw data widely across your organization borders on
negligence – regardless of the security solutions you have in place.
And with anonymous data, PII is removed, but the real transaction data remains unchanged. It’s lower risk than raw data and used more often for both external and internal data activities. However, if a data breach occurs, it is very possible to reverse engineer anonymous data to reveal PII. The security, compliance and privacy risks still exist.
Enter A New Data Paradigm – Synthetic Data
Synthetic data is fundamentally new to the
financial services industry. Synthetic data is the breakthrough data type that
addresses privacy, compliance, reputational, and breach headline risks head-on.
Synthetic data mimics real data while removing the identifiable characteristics
of the customer, banking institution, and transaction. When properly
synthesized, it cannot be reverse engineered, yet it retains all the
statistical value of the original data set. Minor and random field changes made
to the original data set completely protect the consumer identity and
transaction.
With synthetic data, financial institutions
can freely use sensitive data to bolster product or service development with
virtually zero risks. Organizations that use synthetic data can truly dig down
in analytics, including spending for small business users, customer
segmentation for marketing, fraud detection trends, or customer loan
likelihood, to name just a few applications. Additonally, synthetic data can
safely rev up machine learning and artificial intelligence engines with an
influx of valuable data to innovate new products, reduce operational costs and
produce new business insights.
Most importantly, synthetic data helps fortify internal security in the age of the data breach. Usually, the single largest data security risks for financial institutions is employee misuse or abuse of raw or anonymous data. Organizations can render misuse or abuse moot by using synthetic data.
An Untapped Opportunity
Compared to other industries, financial
institutions haven’t jumped on the business opportunities that synthetic data
enables. Healthcare
technology companies use synthetic data modeled on actual cancer patient
data to facilitate more accurate, comprehensive research. In scientific
applications, volcanologists
use synthetic data to reduce false positives for eruption predictions from
60 percent to 20 percent. And in technology, synthetic data is used for
innovations such as removing
blur in photos depicting motion and building
more robust algorithms to streamline the training of self-driving
automobiles.
Financial institutions should take cues
from other major industries and consider leveraging synthetic data. This new
data categorization type can help organizations effortlessly adhere to the
highest security, privacy and compliance standards when transmitting, tracking
and storing sensitive data. Industry revolutionaries have already started to
recognize how invaluable synthetic data is to their business success, and we’re
looking forward to seeing how this new data paradigm changes the financial
services industry for the better.
