Are Life Blood Financial Services driven by Artificial Intelligence or is that a Star-Ship Enterprise Concept?

The sobering ideology being attributed to retiring Westpac Bank Chief, Gail Kelly is compelling;

“There will be times when we can’t wait. Then you’re either on the bus or not. If you are on it and get left behind, you’ll find it. If you are not, you will be lost forever.”

Gail Kelly was not talking about getting to the game. The incredible change she eludes to is the fact that the game is now being played on the bus itself.

As it twists, turns climbs and descends at high speed amongst myriads of other buses in business, at constant risk of crashing; being able to predict where it is going is critical.  That is so, not only to stay in the game, but also so vital to know you’re on the right bus.

A recent McKinsey & Company paper at its heart sends a message that the massive information omnipresence needed to achieve this sort of “real time predictive survive and grow capability” is a now a very real phenomenon.

Dubbed “Big Data”, what is exciting and challenging are the innovative people it is now attracting.  Creative ability and abstractly flexible minds are radically altering how and what gets done with humans no longer doing the work.

Even though it is constantly changing, the fun is about creating the “Big Data” capability.

Its futuristic nature and sheer size make it an exciting journey in uncharted waters.  It is akin to mega ships in high seas bringing vital database power to Kelly buses to run on.

With ever evolving real time detail able be crystallize so very fast, Darwinian disciples are updating theories that survival depends more on an ability to change and not just on being the fittest.

This flip in thinking is also triggering even more challenges to evolutionary theories. Pervasive big data simulations are overturning foundations that previously assumed giraffe’s long necks evolved from stretching for food, as opposed to surviving because they could.

As the buses rush about, innovative brush fires are breaking out and becoming a bush fires. This frenzied momentum to create predicative analysis products is as intense as it is overwhelming.

By using data collected about us, combined with artificial Intelligence to make decisions, this leap forward in passive Information technology is one of the most aggressive of the game changers.

The 1900’s using traditional relational technology, we saw ambitious linked-analytics attempts to define client databases. Even with all the clever recursive logic that could be mustered, it never achieved useful outcomes until interactive real-time client relationship systems came along.

As these intelligent processes were added, the beginnings of the big data metamorphosis saw the joining of disparate pools of information.

The emerging progress that have followed saw Big Data start to grow and take on a life of its own. By watching transactions behaviour it began to learn the relationships. Soon it  started to add to new data by itself which  it then  applied on what were the beginnings of the innovative artificial-intelligent processes we interact with and use today.

Some twenty years on the likes of life Insurance companies,  banks and now even retailers, who provide core community financial services, know it is so vital they understand clients,

Hence they know so much about us and an even shape our ever changing cradle to the grave needs by monitoring us closely. Using business intelligence based data warehouse technology to join all our behavioural client dots with all our transaction data all into one place.

But even so, that may still not be enough for many silo based organizations slow to change. Many now have  such business intelligence services, but they still cannot see or extend on the value. Being at grave risk of driving off a cliff as they take their seemingly invaluable information and either bury it alive or thrown out the window to loose the ability to link opportunities and future markets.

It may seem counter intuitive  to a modern business ideals, but business strategies must now change to remove any the focused blinkers and look to how they can reincarnate to use their seemingly useless data .

Only then will they learn they may already have a gold mine of information about their direct markets and find ways to use it to fast track what is happening as they see and define changes in related data dependencies to spawn entry to connected markets.

Those who already do, are finding they need to change traditional thinking and ways of working. They use well for competitive advantage valuable data, previously junked just to save disk space.

Going back 25 years in the life assurance business example, bank assurance as we know it, was an obvious wholesale connection. That has long since been established in Life offices where Banks partner for a commission as a distribution channel for loan assurance business. And it works very well.

At Colonial Mutual, where I worked in the 1990’s, along with others we followed an ambitious strategy to acquire the State Bank of NSW. The CBA, Australia’s Biggest Bank, watched us achieve that, then acquired Colonial, for the same reason . That was the beginning of a financial services consolidation to redefine what previously seemed unrelated.

In that business like most in mass markets, penetration is all about profile targeting. But even so with high tech tools in developing markets that is still is generally done by localized traditional touch. There, also changing back office system to be able to collect and keep touch data is a key to creating and expanding  into the client relationships networks themselves, is the aim to achieve wider penetration.

In an industry where field agents work practices are driven by deep rooted incentive plans, the trick is getting them to give up the information they traditionally hold back on to protect their commission. The functional teams and strategic thinkers who plan and manage and benefit from them and still forced to work in silos, must also change.

Also in need of a makeover are  risk averse, security and privacy conventions  that typically apply  in financial services, that constrain growth transparency needed for growth of ideas, albeit for valid control,  In the meantime smart operators are innovating to render data anonymous so it can be used. Once pieces are joined and re-conceptualize in wider transactional contexts, the power for artificially intelligent processes to use it is so much more strategic.

Commercial synergy to join and share sparse data relationships is also changing. The barriers are falling to allow going one step further to share and link financial services data directly to the next level.  Hence  the financial services industry is undergoing radical realignment with bundled end to end products that also include derivatives.

The impact is being able to lock in suppliers, with balanced win-win benefits and access to wider markets. On the flip side retailers have done this for a few decades ever since EFTPOS made it possible for them to become quasi banks

In the business process applications space, Client Relationship [CRM], Enterprise Resource  Planning [ERP] Supply Chain Management [SCM] and so on, being well known by acronym, will remain pivotal. Providers however will undergo huge transformations as that market works out how their offerings can cope with and integrate into the new breed in artificially intelligent computers.

The new winners in this arena will be those who can adapt to a world where business relies less on humans and more on tools that make decisions based on artificial intelligence based predictive analysis.

In the meantime, beware of stalwarts from 20th century, who are are still captains of so called modernized organizations, that beneath the waves are still clinker built.  As they continue to train new crews using out-dated ideas to keep things afloat they are dumping heavy stuff, like data.

What they don’t get is the bow waves, from a nimble flotilla already passing them, will not only sink them, and or cut off their access to the buses meeting them at the wharf . The trick is not to be on either when that happens.

Should we rest now that Artificial is actually Real?

Recognition technology now sees computers do much more than manage numbers. This powerful self learning ability matches seemingly obscure connections, to join what neophytes only see as random information.

It is well known that in the last 25 years, as capital productivity grew, labor productivity has remained conversely flat. Sadly the changes to fix the imbalance will be tough on those who worked hard to create what-is now, as that too is being changed by new artificial reality.

Giant leaps forward in the deep learning techniques are enhancing efficiency of all services delivery. Health services are about to turn their ears. That is not only because this is addressing the lack of medical expertise in the world, but it is solving everyday life shortening threats.  Making that possible is hooking innovative artificial intelligence advances cheaply to use what we all already have.

Interpretive computer advances are now so fast that many can now fully self document a processes just by learning from data. Yahoo, now Bing, Facebook, Google and many others, do this with great success, use deep learning robots.

Their Target marketing approach to use information this generates lets the robot systems approach people directly based on what they have leaned about them. Computers have long since been used to find relationships that identify people with potential needs, but now these robots now do the selling.

Using devices attached to your Smartphone to monitor your health a short time ago was seen as an amazing innovative idea, It is now a common place reality.

A chemical prescription may soon be replaced by one for an App which sends data it gathers about you to a computer somewhere. Right now the technology is there to predict with great accuracy heath issues, such as detecting arterial flaking that occurs  in the days and hours before a heart attack  .Being able to send you and your doctor an SMS to say you are in imminent risk of having the heart attack say in the next 48 hours. can save your life

Another good example is curing blindness. caused by disease in third world countries. , Simple interpretive applications use phone camera technology linked to diagnosis tools elsewhere on the planet. These tools, costing less than five dollars, using what people carry or get to easily. Being able to leave these low cost tools with local medical clinics and even patients brings the doctor right to the patient.

“This kind of effective use of artificial intelligence to fix what previously were insoluble problems,” the World Economic Forum says, “is leaping ahead to handle deployment of physicians skills in the developing world that would take about 300 years to train enough people.”

This morning I went to my Pharmacy and they now sell machines to test your blood and others that can be used to shock the heart back to life after an attack.

At the practical level imagine the competitive offerings that will flow from these innovative outcomes. Making the connection with these medical advances clearly change morbidity risk and give the Life offices who relied on it a huge competitive advantage. Those who get there first in populous developing markets, using even 5% Artificial Intelligence capability will gain huge impetus to leapfrog into wider life based financial services.

As industry leaders to start thinking about how to exploit the new options the time is right for community leaders top plan social and economic structures to handle this new reality. Thought leaders who get involved now and extend their creative abilities to put in place strategies to harness the competitive advantage that comes with Artificial intelligence. Winners too will be the fast moving venture capitalists who get on the opportunistic boat to fund it. Traditional cognitive thinkers, even at Ph. D level will need to change or become obsolete.

Nothing is new here as we know at the human end it groups teams in swim lanes of similar skill sets. That has always made it hard to get supply chain flows investment estimates in performance growth very difficult to justify when it follows the ebb and flow of an evolutionary beat.

What is now changing is assuming computers are still dumb. The rate of take up and change indicates that in just five years computers will be off this chart in terms of Artificial Intelligence capability.

If you are not in the game you will have most certainly missed the boat. Embedded deep learning in big data systems already shows artificial Intelligence capability makes growing exponential in real time so the traditional human change and growth rate is no longer an enigma

At the grass roots level many traditional IT people, still think that big data creates big problems. Despite lower storage costs and higher processing power many still constrain database systems administration that by all previous measures already seem to be huge. Good luck with your dreams if you are not able to reorient those guys. That is at the heart of growth issues to be fixed for many companies to progress.

But despair not, as traditionalists will always yield in the end to innovative leaders. I recently had dinner, with a long standing business friend and colleague, who cut his teeth changing processes in Indonesia in the days when change was hidebound in what seemed corrupt and conservatively entrenched.

His success got him a now very high paying VP job which he told me he is leaving to explore artificial Intelligence. Pushing the boundaries of the unknown is what he saw was important and he said also sounds like so much more fun.

We already know systems with embedded artificial intelligence can now do so many things more efficiently by learning from the data. Much more so than humans who spend most of their time being paid to do that.

Creating highly accurate maps is now a few days of work to collect and present pictures to a computer which does the rest, adding street signs distances and so on. Previously that was months of work by a large human team.

And we don’t need Captain Kirk to send a team to find out find out previously disparate SME interests can be easily joined using Artificial Intelligence to determine the common value add. And just one person (perhaps Scotty) can beam it up to the enterprise where deep learning tools can interrogate it further.

As they learn how to focus on and exploit opportunity, partnerships will open up for those on board to not only change business bundling but aid the end user with much more effective and competitive products.

The race now on to build bigger and bigger data vessels with deep learning algorithms to handle and lead that growth.