How Digital Makes Banks Flexible, Responsive And Intimate

While making digital the main channel of customer engagement, banks are also looking to move beyond business as usual, says Amit Anand, a Vice President in Cognizant Consulting’s Banking and Financial Services.

COVID-19 made online channels indispensable for bank customers, including those who preferred in-person banking. This accelerated their digital strategies and created an opportunity to go beyond the basics and become partners in their customers’ pursuit of financial wellness.

As banks bet big on digital, they are looking at technologies such as AI, advanced analytics, and automation to provide personalization, prediction and speed in creating powerful customer experiences. Banks are also increasingly relying on machines to automate repetitive tasks and make complex decisions, creating demand for human skillsets that complement intelligent machines.

Cognizant’s Center for the Future of Work (CFoW), working with Oxford Economics, recently surveyed 4,000 C-level executives globally, including 287 senior banking and financial services executives to understand how banks are adapting to fast and dramatic changes.

The earliest forms of digital banking trace back to the advent of ATMs and cards launched in the 1960s. As the internet emerged in the 1980s with early broadband, digital networks began to connect retailers with suppliers and consumers to develop needs for early online catalogues and inventory software systems.

By the 1990s the Internet became widely available and online banking started becoming the norm. The improvement of broadband and ecommerce systems in the early 2000s led to what resembled the modern digital banking world today. The proliferation of smartphones through the next decade opened the door for transactions on the go beyond ATM machines. Over 60% of consumers now use their smartphones as the preferred method for digital banking.

The challenge for banks is now to facilitate demands that connect vendors with money through channels determined by the consumer. This dynamic shapes the basis of customer satisfaction, which can be nurtured with Customer Relationship Management (CRM) software. Therefore, CRM must be integrated into a digital banking system, since it provides means for banks to directly communicate with their customers.

There is a demand for end-to-end consistency and for services, optimized on convenience and user experience. The market provides cross platform front ends, enabling purchase decisions based on available technology such as mobile devices, with a desktop or Smart TV at home. In order for banks to meet consumer demands, they need to keep focusing on improving digital technology that provides agility, scalability and efficiency.

Seven Ways to Capitalize on Digital

  1. Institute front-to-back digitization. Banks can effectively compete with fintech competitors by becoming digital institutions.
  2. Explore new customer segments and business paradigms. Digital makes it easier than ever for banks to explore small business segments, even as they pursue existing markets.
  3. Emphasize platform centricity and smart aggregation. Open banking standards can help banks to provide personalized products to customers in collaboration with third-party providers and fintechs.
  4. Invest in personalizing the customer relationship. Banks should use personalized experiences to make customers’ lives as frictionless as possible.
  5. Focus on re-building trust and resiliency. Banks need to eliminate any biases in decisions made by machines.
  6. Enshrine inclusivity into your digital strategy. Banks should use digital to reach customers who are left out by being physically and cognitively challenged.
  7. Balance machine-driven and human-centric work. Create sturdy human-machine collaboration by reevaluating jobs for a shared environment.

For more, read our paper “The Work Ahead in Banking: The Digital Road to Financial Wellness”.

Amit Anand is Vice President and North American Practice Leader for Cognizant Consulting’s Banking and Financial Services. Amit has 20 years of experience with firms such as Accenture, Infosys and Cognizant. He has successfully led and managed large business transformation, digital and IT transformation, and associated organizational change management for several financial services clients. Amit is a recognized thought leader with more than 15 publications on topics such as Open Banking, Digital 2.0 and new-age operating models. He can be reached at Amit.Anand@cognizant.com

Manish Bahl leads the Cognizant Center for the Future of Work in Asia-Pacific and the Middle East. A respected speaker and thinker, Manish has guided many Fortune 500 companies into the future of their business with his thought-provoking research and advisory skills. Within Cognizant’s Center for the Future of Work, he helps ensure that the unit’s original research and analysis jibes with emerging business-technology trends and dynamics in APAC, and collaborates with a wide range of leading thinkers to understand and predict how the future of work will take shape. He most recently served as Vice President, Country Manager with Forrester Research in India. He can be reached at Manish.Bahl@cognizant.com

Source: How Digital Makes Banks Flexible, Responsive And Intimate

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How The Power Of Predictive Analytics Can Transform Business

Tableau analytics visual

With the acceleration of digital transformation in business, most CTOs, CIOs, and even middle management or analysts are now asking, “What’s next with data?” and what ongoing role will technology play in both digital and data transformations. Other questions that keep these individuals up at night include:

  • How can people throughout all organizational levels be more empowered to use data and help others make better decisions?
  • What prevents people from more deeply exploring and using data?
  • In what ways can analytics tools and methods help more people use data in the daily routine of business—asking questions, exploring hypotheses, and testing ideas?

With this in mind, plus observations and discussions with many Tableau customers and partners, it seems that today’s circumstances, behaviors, and needs make it the right time for predictive data analytics to help businesses and their people solve problems effectively.

Current realities and barriers to scale smarter decision-making with AI 

With growing, diverse data sets being collected, the analytics use cases to transform data into valuable insights are growing just as fast. Today, a wide range of tools and focused teams specialize in uncovering data insights to inform decision-making, but where organizations struggle is striking the right balance between activating highly technical data experts and business teams with deep domain experience.

Until now, using artificial intelligence (AI), machine learning (ML), and other statistical methods to solve business problems was mostly the domain of data scientists. Many organizations have small data science teams focused on specific, mission-critical, and highly scalable problems, but those teams usually have a long project list to handle.

At the same time though, there are a large number of business decisions that rely on experience, knowledge, and data—and that would greatly benefit from applying more advanced analysis techniques. People with domain knowledge and proximity to the business data could benefit greatly, if they had access to these techniques.

Instead, there’s currently a back-and-forth process of relying on data scientists and ML practitioners to build and deploy custom models—a cycle that lacks agility and the ability to iterate quickly. By the end, the data that the model was trained on could be stale and the process starts again. But organizations depend on business users to make key decisions daily that don’t rise to the priority level of their central data science team.

The opportunity to solve data science challenges

This is where there’s an opportunity to democratize data science capabilities, minimizing the trade-offs between extreme precision and control versus the time to insight—and the ability to take action on these insights. If we can give people tools or enhanced features to better apply predictive analytics techniques to business problems, data scientists can gain time back to focus on more complex problems. With this approach, business leaders can enable more teams to make data-driven decisions while continuing to keep up with the pace of business. Additional benefits gained from democratizing data science in this way include:

  • Reducing data exploration and prep work
  • Empowering analyst experts to deliver data science outputs at lower costs
  • Increasing the likelihood of producing successful models with more exploration of use cases by domain experts
  • Extending, automating, and accelerating analysis for business groups and domain experts
  • Reducing time and costs spent on deploying and integrating models
  • Promoting responsible use of data and AI with improved transparency and receiving guidance on how to minimize or address bias

Business scenarios that benefit from predictive analytics 

There are several business scenarios where predictive capabilities can be immensely useful.

Sales and marketing departments can apply it to lead scoring, opportunity scoring, predicting time to close, and many other CRM-related cases. Manufacturers and retailers can use it to help with supply chain distribution and optimization, forecasting consumer demand, and exploring adding new products to their mix. Human resources can use it to assess the likelihood of candidates accepting an offer, and how they can adjust salary and benefits to meet a candidate’s values. And companies can use it to explore office space options and costs. These are just a few of the potential scenarios.

A solution to consider: Tableau Business Science

We are only at the beginning of exploring what predictive capabilities in the hands of people closely aligned with the business will unlock. AI and ML will continue to advance. More organizations, in a similar focus as Tableau, will also keep looking for techniques that can help people closest to the business see, understand, and use data in new ways to ask and answer questions, uncover insights, solve problems, and take action.

This spring Tableau introduced a new class of AI-powered analytics that gives predictive capabilities to people who are close to the business. In this next stage of expanded data exploration and use, we hope business leaders embrace data to help others make better decisions, and to provide transparent insight into the factors influencing those decisions.

When people can think with their data—when analysis is more about asking and answering questions than learning complex software or skills—that’s when human potential will be unleashed, leading to amazing outcomes. Learn more about Tableau Business Science, what this technology gives business teams, and the value it delivers to existing workflows.

Olivia Nix is a Senior Manager of Product Marketing at Tableau. She leads a team focused on the use of AI and ML in analytics and engagement, including how to use technology to enable more people in organizations to make data-driven decisions. Olivia has been at Tableau for four years where she has worked closely with development teams on new product launches. Prior to Tableau, Olivia worked as an analyst at the Pew Center on Global Climate Change (now C2ES) and Johnson Controls. She has her MBA from the UCLA Anderson School of Management.

Source: How The Power Of Predictive Analytics Can Transform Business

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Critics:

Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions.

The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.

Predictive analytics is used in actuarial science,marketing,business management, sports/fantasy sports, insurance,policing, telecommunications,retail, travel, mobility, healthcare, child protection, pharmaceuticals,capacity planning, social networking and other fields.

One of the best-known applications is credit scoring,[1] which is used throughout business management. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.

Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. The enhancement of predictive web analytics calculates statistical probabilities of future events online. Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining.Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.

For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs.The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.

Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, “Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.”In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization.

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