Despite increasingly stringent data privacy regulations and compliance requirements, data privacy and data intelligence can coexist
The General Data
Protection Regulation (GDPR) in the European Union, the California Consumer
Privacy Act (CCPA) in the United States, and the growing list of global
consumer data protection regulations are putting new pressure on financial
institutions. These data restrictions, combined with the growing
reluctance of increasingly well-informed consumers, are complicating the
process of acquiring data, making it all the more difficult to derive valuable
intelligence. Balancing data privacy and data
intelligence has become a digital house of cards — one that’s fragile, to say
the least, and an issue that the financial sector is struggling to navigate.
Today’s leading organizations that have a lot
to answer for. Voracious and non-strategic collection of data from all customer
touchpoints has created vast lakes of decaying data that offer far fewer
insights than originally promised.
Meanwhile, the cost of managing that data is rising. On top of the
expense of storing and analyzing the data and complying with new regulations,
organizations must hire highly skilled talent.
Beyond the complexity of data management is
the necessity to glean actionable intelligence from the data. For big data
acquisition to be worth the complexity it introduces, organizations need to get
real value from their efforts. Instead, most companies are still just
collecting as much data as they can, and hoping it will give them the
intelligence they need.
companies want to know as much as possible about the customer, so they can
create targeted, personalized
experiences and stand out in a crowded market. Yet customers are often averse
to the idea that those companies are keeping a record of their every thought
and action. This tension is creating a rift in the business-customer
This has to change.
For companies in the financial sector to
benefit from data while addressing consumer privacy concerns, complying with
regulations and all the while providing a frictionless customer experience,, a
less-is-more approach is necessary. The
view that ‘more’ data equals ‘more’ intelligence isn’t always true. In fact,
simply collecting vast quantities of data with no strategy in place for how to
use it can put both the company and the customer at risk.
Without a plan for how to manage and leverage
data effectively to produce actionable intelligence, companies can open the
door to data leaks, breaches, fraud, and security vulnerabilities. Organizations that fall victim to such
attacks face reputational damage, regulatory punishment, consumer backlash, and
— at worst — financial collapse.
To truly benefit from data while achieving
compliance, banks must focus on collecting data that’s highly relevant and
using it to extract insights that are smarter, more focused, and targeted. Although this may sound counter-intuitive,
less data, when processed correctly can deliver improved insights into customer
behavior and engagement, enabling a financial financial institution to take the
necessary steps to differentiate and disrupt.
The In “Centralizing Intelligence with
Advanced Feature Engineering,” Dr. Yinglian Xie, cofounder and CEO of
DataVisor, examines the best platform and approach to drive deeper data
intelligence and results. The ebook focuses on how companies can leverage
relevant data through features engineering, an essential process for building
advanced machine learning models.
Leveraging this approach, you can analyze aggregated datasets with precision.
For example, you can calculate the total number of transactions processed from
a particular device where the amount of each transaction exceeds a specified
threshold. This approach offers significant insight into business and customer
behavior — and you don’t need individual transaction amounts to see the
Holistic data analysis at the group level
enables financial institutions to unlock trends, patterns, and commonalities
and uncover actionable insights that wouldn’t be visible at an individual
level. Derived data can also determine multiple additional features from one
single data point for even richer intelligence. With these techniques,
companies can better align their efforts with evolving big data regulations,
while improving the quality and depth of the data.
Leaders and innovators in the banking sector
are under far greater regulatory pressure than many other industries, and, as a
result, must harness technologies such as machine learning and artificial
intelligence to get more value with less data. To this end, a new approach to
data analysis that reduces risk while maximizing data efficiency and customer
acceptance emerges. This enables organizations in the financial services
industry to balance ethics and regulatory compliance with intelligence to
create cohesive solutions that maximize results.