Almost Painless Identity Verification, Businesses Championing Sustainable Development, and Transforming Risk Management

Special Holiday Message from Marcia Tal

As you know, we publish FRAMEWORK bi-monthly. We hope you enjoy reading each issue as much as we enjoy researching and providing interesting, challenging content and perspectives.

Our next publication would be September 22—which happens to be the Jewish New Year. As I observe this holiday, which holds significant meaning to me and my family, I will be releasing our next FRAMEWORK issue on September 29.

For those who observe the holiday, I wish you a happy and healthy New Year!

Thank you all for your continued readership and feedback on our FRAMEWORK newsletters.

Tal Solutions’ FRAMEWORK newsletter presents you with a bi-monthly collection of curated, personally selected content and media focused on big ideas about and beyond Data Analytics—all with Marcia Tal’s insightful point-of-view and commentary.

FRAMEWORK : Voice of Consumer

Data Analytics Helps Ease the Pain of Identity Verification

How many times have you been seriously irritated by proving you are who you say you are?

Say you’re on the phone with a customer service representative—and before you can get to the problem you called about, you need to verify your identity.

Sam Ransbotham’s “Improving Customer Service and Security with Data Analytics” explores the trade-offs between security and service—and how new approaches to analytics are helping improve security without creating a negative customer experience.

Ransbotham illustrates the problem by examining the “adversarial by design” identity verification process we go through to report a lost debit card. “Even the name ‘security challenge question’ evokes a combative stance” and the caller “is not trusted until passing through a gauntlet.”

Very frustrating, right?

Here’s where we can use data analytics and technologies to transform a negative experience into a positive one:

“Data and machine learning, specifically speech processing, offer a great example of an invisible way that analytics can simultaneously help improve security and service.”

Financial institutions like Fidelity, HSBC and Barclays are now testing or using voiceprints for customer identity verification, and they are beginning to realize savings in employee time and convenience.

Valuable business impact will surely come from the application of data and machine learning that ensures that “the customer interaction begins by focusing on assistance rather than challenge.”

Yet, businesses rarely quantify customer dissatisfaction and understand its business impact.

Knowing how to quantify and measure that impact is what’s even more important—for both businesses and consumers.

Let’s outline an analytic process that could help businesses better address customer dissatisfaction:

  • Identify customer pain points and diagnose the extent of the dissatisfaction
  • Use intelligent technologies to gauge sentiment and better understand dissatisfaction
  • Based on learnings, devise ways to predict dissatisfaction—and consequent negative behavior
  • Identify actions to reduce dissatisfaction and gauge the business costs and impact

The outcome of this process will be the creation of analytic tools and technologies capable of providing new insight and understanding to the downside of customer experience.

We have the data—from everyone’s frustrating identity verification to a myriad of consumer dissatisfactions—now let’s work to transform those unsatisfactory experiences into positive ones.

FRAMEWORK : Social Enterprise

See the Interconnectivity of Business Sustainability and Planetary Sustainable Development

Saving the planet is a big job.

Where to begin?

Bhaskar Chakravorti tackles this question in his article “How Companies Can Champion Sustainable Development”—and I provide an analytical framework for illustrating the interconnectivity between business sustainability and sustainable development.

Chakravorti believes the UN’s Sustainable Development Goals (SDGs) “provide a framework for mobilizing companies to invest in sustainable development in an ongoing way, while also pursuing their own business interests.”

Supported by the Citi Foundation, Chakravorti’s research group from the Tufts University Fletcher School launched a yearlong research effort spanning 2015–2016 “to study inclusive innovators spanning 10 industries.”

The group found the challenge of using the SDGs to be “simply dealing with their sheer breadth.” For example, how do you approach goals as big-picture as “no poverty” and “peace and justice”?

Among the primary findings was the “key lesson at the very outset: figuring out how to begin.”

What the research recommends is a three-step analytic approach to sustainable development:

  • “Segment the SDGs
  • Identify where the company fits
  • Make the business case.”

Here, I’d like to step back and visualize. Imagine an infinity loop that balances sustainable development and business sustainability.

Within this framework, you see an expanded analytic approach that results in aligning measurable outcomes for sustainable development and business sustainability.

The infinity loop shows the fluid integration and interconnectivity of business sustainability with sustainable development. You see the back-and-forth flow of common and critical elements—relevance, strategy, impact. Many businesses grow because they are focused on sustainability goals that align their brand values and corporate actions with those of their customers and clients.

Saving the planet—and helping all its people—is still a big job. As companies learn where to start, they will make meaningful progress toward sustainable development and their own business sustainability.

FRAMEWORK : Data Analytics

What Will It Take to Digitally Transform Risk Management?

“What’s the risk in transforming risk management?” is not a trick question.

In their article, “Digital Risk: Transforming risk management for the 2020s,” Ganguly et al. explore the coming digital transformation in the most protective of business disciplines.

The writers define “digital risk” as a “term encompassing all digital enablement that improves risk effectiveness and efficiency—especially process automation, decision automation, and digitized monitoring and early warning.”

They elaborate: “Essentially digital risk implies a concerted adjustment of process, data, analytics and IT, and the overall organizational setup, including talent and culture.”

As such, the magnitude of change is considerable.

The authors see three areas of change: processes, data, and organization, and they detail approaches for each. As a result, the potential “benefits of digital risk initiatives include efficiency and productivity gains, enhanced risk effectiveness, and revenue gains.”

The report identifies three specific areas that are “optimal for near-term efforts: credit risk, stress testing, and operational risk and compliance.”

Using capabilities and processes you’ve built in your digital environment to execute these risk management areas doesn’t mean that you are changing the discipline of risk management itself. 

Risk management has always relied on time to validate processes. For example, in digital development, moving from testing to production will still demand validation of risk performance and regulatory requirements.

As complex and critical as risk management is, the discipline certainly needs automation at least and enterprise wide transformation at best.

Yet digital transformation will be a “multiyear journey.” As the authors explain:

“Because of its highly sensitive environment, risk is digitized end to end over a longer timeline than is seen in customer-service areas. Specific capabilities are developed to completing and release discretely, so that risk management across the enterprise is built incrementally, with short-term benefits.”