Personal Financial Wellness: A Banking Opportunity And Imperative

Financial wellness at a personal level..Cognizant

Plenty of banks offer financial wellness tools. But by personalizing these offerings, they can support customers while also reinvigorating their brand, notes Andrew Warren, a leader in Cognizant’s UK & Ireland Banking and Financial Services practice.

With the explosion of digital banking during the pandemic, financial institutions (FIs) have their sights set high for growing revenues through digital channels. To do that, however, they’ll need to become more intertwined in and relevant to the daily lives of their customers.

One way to achieve this is by taking all the information they have about their customers and turning it into services that enhance customers’ financial wellness. Doing so would be especially welcome in the post-pandemic landscape and the financial uncertainty it has spurred.

To put it into perspective, more than 40% of UK adults are now considered “financially vulnerable,” and nearly 40% of UK adults have seen their financial circumstances deteriorate since February 2020.

Financial wellness at a personal level

While almost every bank now offers financial wellness tools of some sort, whether a budgeting app, a financial literacy course or education on financial planning, these services are generalized to all customers rather than addressing individuals’ personal financial wellness needs. Because financial situations vary greatly across individuals, the truly personalized element is vital.

For example, to help individuals build their savings, banks can use predictive AI technologies to understand customer transactions and cash flow patterns and automatically divert the right amount of extra funds to savings or investments. Royal Bank of Canada says it has helped clients save an average of $358 per month with this type of tool.

Further, by mining debit card data, banks can gain insights into spending habits that they can share with customers or incorporate into personalized offerings. Through AI-driven interventions, personalized “nudges” and micro-level targeting, banks can ensure customers take the steps needed to embrace their financial destiny.

Data and AI underpinnings

Achieving a new hyper-personalized banking future requires the right customer data and digitizing front-to-back-office operations. The financial services industry is arguably the most data-intensive sector in the global economy. Banks, in particular, have enormous amounts of customer data (e.g., deposits/withdrawals, POS purchases, online payments, KYC profiles). Historically, however, they haven’t been very good at utilizing these rich datasets.

By harnessing data responsibly — taking into consideration ethical and regulatory aspects, and striking the right balance between machine-driven and human-centric work — FIs can identify and serve new customer segments of one.

At HSBC, for example, customers can begin a conversation in the bank’s mobile app with an AI chatbot, answering simple questions immediately. Complex inquiries get passed to front-line colleagues. The AI system provides agents with details on the issue and guidance on how to resolve it.

We’ve been working with FIs to pull history and experience via AI into the conversation. We use emotion recognition tools (e.g., intelligent real-time language processing and sentiment analysis) to better understand the context of a customer’s inquiry and when a customer might be vulnerable, even if they’re not aware of it themselves.

This type of information can be harnessed to create personalized engagement plans that customize interactions. These tools can also enable banks to match agents’ personality and strengths with customers to help build deeper, more trusted customer relationships.

Time for a new approach

In addition to the customer benefits of personalization, banks can also realize cost savings and performance gains. According to Boston Consulting Group, hyper-personalization can lower rates of customer churn and boost sales, leading to annual revenue uplifts of 10%.

However, there’s plenty of work to do. Right now, only 45% of consumers are satisfied with the quality of personalized services they receive from their banks.

Here are three key ingredients for progressing toward personalized banking capabilities:

1. Technology

Banks need a mashup of data analytics, AI and automation to deliver personalization, intelligence and predictions at speed. This requires harnessing high-quality data to feed AI/ML algorithms so they can deliver experiences and services that anticipate customers’ needs, wants and desires, as well as combining automation with human supervision to build trust and empathy.

For example, we are working with FIs to link up data sources and introduce AI-powered algorithms that provide a dynamic view of a customer’s financial profile. This helps customers with their financial wellness plans and enables FIs to suggest products that enhance customers’ financial wellness.

2. Talent

Along with adroit technologists, banks need designers, anthropologists, ethnographers and others with social science pedigrees to apply human-centric design thinking to product and service development.

Social scientists are equipped to understand and apply the complexities of human behavior to help FIs predefine user personas and scenarios and use these to shape and personalize the experiences they provide.

In this spirit, we are helping a European state-owned organization to implement a user-centered digital product and service offering, including completely reengineering its mobile banking app. The existing app was plagued by a number of challenges, including a dated technology platform, poor user experience, low adoption and high cost-to-serve across non-digital channels.

We helped the organization define its go-to-market, mobile-first digital strategy, identifying key market-facing propositions based on user research as well as delivering these propositions at scale. Multiple subject matter experts were involved in the project from the outset to ensure a holistic approach, including program/project managers, scrum masters, data architects, human-centric design leads, UX and UI designers, researchers, developers, business analysts and process improvement specialists.

With this project, the bank aims to significantly increase its customer base over the next five years, by combining customer trust with a best-in-class user experience on its re-engineered mobile app, which puts it on par with challenger banks.

3. Culture

To drive change, customer well-being and digitization need to become part of the FI’s culture. This is more than merely embracing Lean methodologies and/or more flexible ways of working. Helping customers achieve financial wellness demands a fresh outlook that maximizes new, agile ways of working and data-driven approaches embedded enterprise-wide — from leadership down to every employee.

Turning the tables

For banks whose business models are based on profiting from customers’ lack of financial wellness (overdrafts, fees, charges, etc.), it can be challenging to adopt this approach. The majority of banks have a high cost-to-serve, with legacy infrastructures and high overhead.

In the face of tough competition from savvy fintechs and non-bank disruptors, however, it’s clear that incumbents urgently need to pivot their approach. FIs that incorporate personalized financial wellness into their business strategy have an outstanding opportunity to support their customers while also reinvigorating their brands — and realize growth and cost savings at the same time.

Andrew Warren

By:

Andrew Warren is Head of Banking & Financial Services and a member of the Executive team at Cognizant UK & Ireland. He leads a team responsible for driving results for Cognizant’s financial services clients……

Source: Personal Financial Wellness: A Banking Opportunity And Imperative

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Digital Transformation Depends on Diversity

Across industries, businesses are now tech and data companies. The sooner they grasp and live that, the quicker they will meet their customer needs and expectations, create more business value and grow. It is increasingly important to re-imagine business and use digital technologies to create new business processes, cultures, customer experiences and opportunities.

One of the myths about digital transformation is that it’s all about harnessing technology. It’s not. To succeed, digital transformation inherently requires and relies on diversity. Artificial intelligence (AI) is the result of human intelligence, enabled by its vast talents and also susceptible to its limitations.

Therefore, it is imperative for organizations and teams to make diversity a priority and think about it beyond the traditional sense. For me, diversity centers around three key pillars.

People

People are the most important part of artificial intelligence; the fact is that humans create artificial intelligence. The diversity of people — the team of decision-makers in the creation of AI algorithms — must reflect the diversity of the general population.

This goes beyond ensuring opportunities for women in AI and technology roles. In addition, it includes the full dimensions of gender, race, ethnicity, skill set, experience, geography, education, perspectives, interests and more. Why? When you have diverse teams reviewing and analyzing data to make decisions, you mitigate the chances of their own individual and uniquely human experiences, privileges and limitations blinding them to the experiences of others.

One of the myths about digital transformation is that it’s all about harnessing technology. It’s not.

Collectively, we have an opportunity to apply AI and machine learning to propel the future and do good. That begins with diverse teams of people who reflect the full diversity and rich perspectives of our world.

Diversity of skills, perspectives, experiences and geographies has played a key role in our digital transformation. At Levi Strauss & Co., our growing strategy and AI team doesn’t include solely data and machine learning scientists and engineers. We recently tapped employees from across the organization around the world and deliberately set out to train people with no previous experience in coding or statistics.

We took people in retail operations, distribution centers and warehouses, and design and planning and put them through our first-ever machine learning bootcamp, building on their expert retail skills and supercharging them with coding and statistics.

We did not limit the required backgrounds; we simply looked for people who were curious problem solvers, analytical by nature and persistent to look for various ways of approaching business issues. The combination of existing expert retail skills and added machine learning knowledge meant employees who graduated from the program now have meaningful new perspectives on top of their business value. This first-of-its-kind initiative in the retail industry helped us develop a talented and diverse bench of team members.

Data

AI and machine learning capabilities are only as good as the data put into the system. We often limit ourselves to thinking of data in terms of structured tables — numbers and figures — but data is anything that can be digitized.

The digital images of the jeans and jackets our company has been producing for the past 168 years are data. The customer service conversations (recorded only with permissions) are data. The heatmaps from how people move in our stores are data. The reviews from our consumers are data. Today, everything that can be digitized becomes data. We need to broaden how we think of data and ensure we constantly feed all data into AI work.

Most predictive models use data from the past to predict the future. But because the apparel industry is still in the nascent stages of digital, data and AI adoption, having past data to reference is often a common problem. In fashion, we’re looking ahead to predict trends and demand for completely new products, which have no sales history. How do we do that?

We use more data than ever before, for example, both images of the new products and a database of our products from past seasons. We then apply computer vision algorithms to detect similarity between past and new fashion products, which helps us predict demand for those new products. These applications provide much more accurate estimates than experience or intuition do, supplementing previous practices with data- and AI-powered predictions.

At Levi Strauss & Co., we also use digital images and 3D assets to simulate how clothes feel and even create new fashion. For example, we train neural networks to understand the nuances around various jean styles like tapered legs, whisker patterns and distressed looks, and detect the physical properties of the components that affect the drapes, folds and creases. We’re then able to combine this with market data, where we can tailor our product collections to meet changing consumer needs and desires and focus on the inclusiveness of our brand across demographics.

Furthermore, we use AI to create new styles of apparel while always retaining the creativity and innovation of our world-class designers.

Tools and techniques

In addition to people and data, we need to ensure diversity in the tools and techniques we use in the creation and production of algorithms. Some AI systems and products use classification techniques, which can perpetuate gender or racial bias.

For example, classification techniques assume gender is binary and commonly assign people as “male” or “female” based on physical appearance and stereotypical assumptions, meaning all other forms of gender identity are erased. That’s a problem, and it’s upon all of us working in this space, in any company or industry, to prevent bias and advance techniques in order to capture all the nuances and ranges in people’s lives. For example, we can take race out of the data to try and render an algorithm race-blind while continuously safeguarding against bias.

We are committed to diversity in our AI products and systems and, in striving for that, we use open-source tools. Open-source tools and libraries by their nature are more diverse because they are available to everyone around the world and people from all backgrounds and fields work to enhance and advance them, enriching with their experiences and thus limiting bias.

An example of how we do this at Levi Strauss & Company is with our U.S. Red Tab loyalty program. As fans set up their profiles, we don’t ask them to pick a gender or allow the AI system to make assumptions. Instead, we ask them to pick their style preferences (Women, Men, Both or Don’t Know) in order to help our AI system build tailored shopping experiences and more personalized product recommendations.

Diversity of people, data, and techniques and tools is helping Levi Strauss & Co. revolutionize its business and our entire industry, transforming manual to automated, analog to digital, and intuitive to predictive. We are also building on the legacy of our company’s social values, which has stood for equality, democracy and inclusiveness for 168 years. Diversity in AI is one of the latest opportunities to continue this legacy and shape the future of fashion.

By: Katia Walsh

Source: Digital transformation depends on diversity | TechCrunch

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Avoid These 5 Phrases That Make You Sound ‘Passive Aggressive’: Body Language Expert

Today, we live in a world where business, degrees and even entire relationships are conducted behind a screen. As a result, employee frustration and miscommunication is at an all-time high, with tone alone being misinterpreted almost half of the time in email, leading to endless wasted hours and heightened anxiety.

For better or worse, digital communication, whether it’s through email or direct messages on platforms like Slack, don’t let us see each other’s immediate reactions — which is why we look for ways to “politely” express irritation. The key word is “politely,” but it isn’t always interpreted that way.

So let’s take a look at the five most common phrases employees use that actually make them passive aggressive and petty:

1. “Per my last email…”

What it actually means: “You didn’t really read what I wrote. Pay attention this time!”

2. “For future reference…”

What it actually means: “Let me correct your blatant ‘mistake’ that you already knew was wrong.”

3. “Bumping this to the top of your inbox…”

What it actually means: “You’re my boss [or employee]. This is the third time I’ve asked you. I need you to get this s*** done.”

4. “Just to be sure we’re on the same page…”

What this actually means: “I’m going to cover my a** here and make sure that everyone who refers to this email in the future knows that I was right all along.”

5. “Going forward…”

What it actually means: “Do not ever do that again.”

It’s likely that you’ve used one of these phrases before without even realizing that it could be perceived as passive aggressive. Or, you may have been on the receiving end, which can also be frustrating.

(Even as a digital body language researcher, whenever I see “Thanks for your patience” in an email, I can’t decide if they’re brushing me off with an undefined future date, or if they really only need a few days longer than expected to get back to me. In most cases, though, I know they’re just saying “Sorry I’m late with this; it’s taking longer than I thought.” That’s all.)

The right way to express what you mean

So how should we frame our own “Just following up on this” without engaging in any passive aggressiveness? When is it okay to loop in our boss without seeming like a jerk? When do we use the phone to call and clarify something?

Here are four things successful communicators do:

1. Don’t respond to messages or emails when you’re angry or frustrated.

This prevents miscommunication, wasted time and regret. If you feel emotionally hijacked, save your email message as a draft and revise and send it when you’re in a better mood.

2. Assume good intent.

Instead of calling someone out for screwing up, step into their shoes and ask yourself, “What are some reasons why they might have made this mistake?”

It’s better to people exactly what they need to take action. Sometimes just adding a quick brief so that they don’t have to go back and read through previous emails and writing “Here’s what I need from you” or “Here are the open dates again” is helpful.

3. Show empathy and encouragement.

Replace imperative words like “Do this” with conditional phrases like “Could you do this?” When delivering feedback, begin your message by expressing appreciation using words like “Thank you for [X]” or “Excellent job on [X].”

If your boss, or even a client, sends you a passive aggressive email, fight that urge to send an even more petty reply. Lowering your actions down to their level will only escalate the tension and increase anxiety.

4. Avoid digital ghosting.

Need to get back to someone? Here’s a quick guide to remember:

  • If you can answer in 60 seconds or less: Respond immediately.
  • If it’s urgent: Respond immediately or let the sender know you are working on it. Make an appointment with yourself on your calendar if you need to.
  • If it’s a matter lacking urgency: Don’t stress; block out time to follow up after at your convenience.
  • If you’re the one waiting for a response: Unless it’s critical that you get a reply ASAP, remember that people may have a lot on their plates. If you follow up twice and don’t get a reply, switch to a different medium (e.g. a phone call).

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By: Erica Dhawan, Contributor

Erica Dhawan is a leadership expert, keynote speaker and author of “Digital Body Language: How to Build Trust and Connection, No Matter the Distance.” She is also the founder and CEO of Cotential, a company that has helped leaders and teams leverage collaboration skills globally. Her writing has appeared in publications, including Fast Company and Harvard Business Review. Follow her on Twitter @ericadhawan.

Source: Avoid these 5 phrases that make you sound ‘passive aggressive’: Body language expert

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