A technology led game plan for millennials

The share of middle-class households in the US has decreased from 61% in 1971 to 51% in 2019 and the gap between the upper- and lower-income households has increased. India would soon have a middle class that is proportionately as large as that of the US today. Despite being better educated, millennials are behind boomers and have “lower wages and fewer fringe benefits.” They have non-linear careers — software engineer by day and blogger/tutor by night is a prevalent millennial career path. More than one-third of them have a side job. They want personalized experiences, they want information, control their future, and work with financial advisors and service providers who understand them and have customized solutions. On the other hand, they are likely to live longer — the average human life in India is on the rise (though COVID has put a stick in it for now). Longer life span and sustained ailments increase healthcare costs and policy changes to accommodate these needs. As the millennials enter the workforce in this decade, they are about to change employment and eventually retirement planning as we know it. Automation technologies have helped to address the challenges in the finance and accounting industry and helped in transforming banks, insurance firms, and accounting service providers around the world.

AI for hyper-personalized investment schemes

Personalization is the way of life for millennials. When it comes to retirement planning, many seek personalized portfolio management that is based on real-time metrics such as age, gender, salary, current accounts, rate of contribution, short- and long-term milestones. Advanced AI technologies enable plan administrators to offer such hyper-personalization — for every individual — at scale. In the past few years, customer-facing industries such as banking have strengthened their technology prowess to provide hyper-personalized services. Today’s AI and machine learning capabilities automatically create self-learning models – efficiently and in real-time – so that customers get the simplest possible contextual experience with each interaction. By understanding the individual needs of consumers, more compelling and interesting experiences can be created. Artificial intelligence and machine learning offers the added edge of mimicking human judgment while retaining the disciplines of rule-based investing. Investment strategies employing such grounds to leverage market opportunities tend to perform better while reducing bias errors.

Automation for consistent savings and investing

The generation of go-getters might learn the importance of saving money from YouTube videos, and they use mobile apps, fintech and digital solutions to inculcate a savings habit. Today, some apps automatically transfer a fixed amount each month to savings; increase savings year-on-year based on inflation, age etc.; save spare change from every transaction into a fund; send automatic reminders and alerts etc. It is also important for financial services organizations to re-think their brand image to address millennials, GenX and the ones in the future. The younger generation believes in the greater good for all, diversity and inclusivity. To attract and retain customers from this generation, retirement administrators also need to leverage technology to connect with them.

Drive engagement through financial literacy

The next generation is information-savvy, and they also like to feel connected to their investments. The best way to establish engagement is to offer financial learning resources, through the medium relevant to them, in the form of blogs, podcasts, ebooks, newsletters etc. You can also interest the socially conscious millennials in environmental, social and governance funds, recommending investments that align with their values.

Advanced analytics to serve various demographic

Millennials are happy sharing data if it means they get an intelligent solution to their problems. Retirement plan technology can leverage this data using artificial intelligence and machine learning tools to identify patterns that help understand the retirement needs of diverse groups. By building dynamic analytics solutions, plan sponsors and administrators can adapt to their participants’ changing needs without executing disruptive large-scale transformations.

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