Author | Pankaj Jain
Remember the dot com era of lofty valuations of anybody who had a website? Even truck based logistics transportation companies renamed themselves as “so and so Technologies Inc.” to mislead investors who were high on the technology euphoria, every Cobol programmer’s resume had HTML and Internet programming languages written all over. Notwithstanding the euphoria, markets eventually crashed in the absence of real earnings. This is how the XLK (Tech Sector) crashed 79%, and in the ensuing cascading effect, the NDX (100 largest stocks in the Nasdaq Composite) crashed 82%.
Fast forward 20 years, it is like Déjà vu. Tech stocks are up 43% in last 2 years, 7 of the world’s 10 most valuable companies are in the Tech sector, matching the late 1999 peak.
In order to ride this euphoria, every company is going digital with Big Data initiatives — every traditional software programmer’s resume has been transformed into a Data Scientists’, Bitcoins, the greatest frenzy since the tulip bulb mania, have just been forecasted to reach $100,000 in the near future.
Historically, the greatest companies, too, reach exuberant overvaluations during the blow-off stage of the manias.
Even though technology has become sophisticated and data has become the new oil and is riding high on unlimited computing power through cloud pipelines, its implementation and use has become much less complex. However, it is surprising to know that majority of the companies are still reeling under the technology complexities of 1990s, thereby meeting huge resistance to any change initiative to become agile.
As a result, companies together have spent billions to capture every bit of data they can get hold of in the form of data lakes and data warehouses but have no real business impact to show. Simply becoming a digital enterprise or creating data lakes or enabling omni-channel interactions isn’t enough. Today’s customers expect a Google or Amazon or Uber like experience. Such experiences can only be delivered through deep contextual intelligence on every customer.
The question here is — How do companies drive innovation and harness these technologies to deliver customer delight and bottom-line impact to justify the lofty valuations?
It turns out that there is a silver lining amidst all the deafening noise emanating from ubiquitous phrases such as big data, digital transformation, machine learning and artificial intelligence. Wealth Management is an industry which is on the forefront of leveraging this disruption where artificial intelligence (AI) and human intelligence can work together to create greater consciousness for customer delight and bottom-line impact.
Companies need to think about developing a ‘Customer Consciousness’ by using a cross section of data related to their customers.
This is how Wealth Management is harnessing the power of human-machine synergy in order to deliver better results than either humans or AI alone: Machine learning is used to match investment possibilities to client preferences. There are far too many investing options today and there is a greater need for timely response to black swan events in the marketplace — for example, the US Presidential election in 2016 resulting in sectors which could benefit from deregulation such as banks & healthcare, the Brexit vote and the resulting decline in UK-based stocks etc.
In most existing software systems, the recommended investments are strictly passive, that is, mutual funds or exchange-traded funds. However, Machine based deep learning can offer mutual funds if the client prefers them, but can also present individual stocks or bonds based on the firm’s research. The portfolio manager is given several ideas and can use his/her own judgement to use any or all of them.
Leverage leading indicators for deeper market understanding & develop new signals for investment decisions
In order to make all of the firm’s investing knowledge available through the system, machine learning based artificial intelligence could take the knowledge embedded in investment analyst reports and make it available to support the choices presented to portfolio managers. It involves working with the firm’s research department to try and make the knowledge in reports more structured and consumable by machines.
Tech companies will need to make business use cases like the ones adopted by Wealth Management industry alive with agility in order to keep justifying such lofty valuations in the days to come.