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  • @fintechna 10:13 pm on March 1, 2017 Permalink | Reply
    Tags: artificial intelligence, , ,   

    Three Steps to Adopt Artificial Intelligence in Banks and Insurance 

    Three Steps to Adopt Artificial Intelligence in Banks and Insurance fintech


    Today, there is incredible interest in anything that is even remotely related to (AI). AI is dominating the conversation on a variety of levels. Philosophers and thinkers are debating the moral implications and risks for human kind of a world where intelligent machines are ubiquitous. In the media, it seems that a new movie or TV series on AI is launched every month. Academic papers on the topic are receiving attention from far beyond the scope of the usual research audience. In the meantime, businesses are facing much more short-term and concrete decisions about what to do with AI, where to invest first and how to measure the return of these investments.

    In this post, I will try to define a framework for how to structure decisions about AI adoption. To do so, I will focus on a sector that for different reasons is an early adopter: Banking and .

    As with any innovation, it is very important that established organizations focus first on selected “easy wins” or incremental change instead of disruptive change. This is a solid approach for several reasons. It gives you the chance to take advantage of some concrete benefits in the short term, which helps mitigate internal resistance, and it provides a view of the innovation’s full potential.

    For these reasons, using AI to increase automation for certain operative tasks is a great way to start.

    The daily operations of Banking and Insurance companies contain a number of repetitive, human error-prone processes that make them a perfect target for initial adoption of AI. Here, you have the added advantage that, even if you decide to start small and with only partial automation, the ROI could be staggering because these are typically high-consumption processes in terms of resources and costs.

    Three Steps to Adopt Artificial Intelligence in Banks and Insurance fintech

    Let’s consider some processes that are common to both industries that fit these requirements. Customer care and e-form extraction (reusing data taken from electronic forms), or banking-specific processes such as mortgage approval (moving data from different documents into a central repository to do calculations) or fraud detection (tracking account activities, communications, connections etc.) are processes that are ripe for automation. In insurance, this includes processes such as claims management, new policy quotes, or the technical due diligence required for a new commercial policy.

    In processes like customer care automation or claims management, you can achieve significant savings even if you limit the scope to partial automation. In addition, the standardization often brings improvements for “soft” metrics like customer satisfaction. Each manager knows how many resources are dedicated to these tasks, so the potential ROI would be easy to calculate. A collateral benefit is that resources and people freed from these activities can be assigned to higher value tasks, such as sales-related activities that focus on growing the top line.

    Once the organization has experimented with the initial adoption of AI by focusing on high ROI initiatives linked to incremental improvement, it’s time to focus on the areas that can bring the most strategic value. This is what AI was made to do.

    The MIT Sloan Management Review recently published an article where they try to get at the core of what AI can really provide. They compared AI to the advent of the computer. The authors make the case that, even if the computer brought drastic change to basically everything, the real transformative element was the improvement in calculation.

    For AI, the elements that will bring about real change are scalability and cheap prediction power. Once automation is out of the way, these are the areas where organizations should start focusing.

    Banking and Insurance companies could make a smooth transition to AI-enabled predictions by starting to leverage data made available by the automated processes. For example, a side result of customer care automation is an increased set of analytics about your customer. Frequency of communications, the topics discussed, customer reactions to the marketing message, etc. are quantitative and qualitative data that can help in creating (again through AI and machine learning algorithms) models to predict customer behavior such as propensity to churn or to buy, to promote with peers, etc. Similar benefits can be achieved with processes such as claims management or technical due diligence for new policies.

    Organizations that have gone through the initial steps of adoption will be ready for the third more disruptive step. Let’s go back to the computer example from the MIT article. The majority of people who were in awe of the computer completely ignored or dismissed the larger impact that this speed and precision in calculation would have on us in the future via the  internet, e-commerce and free video phone calls.

    With AI, we are at the same stage of computers in the 70s. The most disruptive effect for organizations will be in the appearance of new business models, especially in the most traditional sectors (just as retail and telecommunications were among the core sectors disrupted by the evolution of computers).

    Going back to our focus on Banking and Insurance, while these organizations are going to be busy going through the first two steps in AI adoption, it is important to not forget about the big changes that are coming. For example: What will autonomous driving do to insurance? What would highly accurate algorithmic predictions of movements in the financial market (for example considering unstructured data like news, social feeds, etc. in addition to stock price fluctuations) do to ? What will perfect weather forecasts do to banks and insurance companies? No matter how advanced a company is in adopting automation or in experimenting with prediction, not paying attention to these next aspects of AI will be a death sentence for any business.

    AI adoption will be about competitive strength first and business sustainability later. As some of the early adopters of this , Banking and Insurance companies must ensure that they have a strategic framework in place to support the full cycle of these changes. If they drive this adoption through smart, incremental and forward-looking tasks they have a good chance of obtaining both short and long term gains.

  • @fintechna 8:07 pm on June 14, 2016 Permalink | Reply
    Tags: , artificial intelligence, , machine learning,   

    I’m calling B.S. on A.I. 

    Im calling B.S. on A.I. fintech

    Sitting on the panel at today’s ASIFMA capital markets conference in Hong Kong, I had a small epiphany. Or minor brain malfunction, not sure which.

    We need to stop talking

    about .

    By “we” I mean anyone involved in Finance or Fintech. Shut. Up. If you work in a field with real A.I. applications such as image processing, robotics, industrial automation or such, keep pretending like you know what you’re talking about. Carry on.


    Why are we even talking about A.I. in the first place? I blame investors. To the lay man, which let’s face it most investors are, A.I. sounds magical. A bit of magic turns a regular business plan into the next big thing. It’s like “Turbo” in the 80’s. “eAnything” in the 90’s. “Big Data” in the 2000’s.

    A.I. is the new Big Data

    Nobody knows what it is, but everyone thinks it’s good, and therefore claims their doing it. The next time you’re at a startup pitch session, make a count of how many have the word “” on a slide. It will be high. Then ask them how many of their staff have experience in Neural Networks. It will get quiet. It will be awkward. They thought no one would ask.

    Algo is not A.I.

    The reason a lot of these companies are tagging their selfies with machinelearning is that they have some cool algorithm. Sweet I.P. bro! News flash: algos are not intelligent. Algos take in data that you hand-picked, and probably pre-formatted, complete some operation you specified explicitly, and produce results which are predictable. Intelligence is not predictable. Intelligence does whatever IT thinks is best.

    Im calling B.S. on A.I. fintech

    The reason we should stop talking about A.I. in the context of Finance is really simple. Would your compliance department be comfortable with the idea, that nobody knows exactly what decisions are being made with your customers money? And the regulator? Yeah, thought so. That’s what artificial intelligence means. You don’t want that. You can’t handle that.

    Great for gambling

    If the only thing you care about in the world is investment performance, then sure get into A.I. and go all in. In a zero interest rate, semi-efficient global market opportunities for outsized returns are like needles in a haystack. So to justify a typical hedge fund fee of 2+20, which is two percent of all your money every year, plus a fifth of any returns you make, you need to be creative. What’s more creative than an intelligent machine? Probably a human, for now, but stick with me here.

    Im calling B.S. on A.I. fintech

    You new fund managers

    Most hedge funds are increasingly becoming tech companies. Less suits and cigars, more t-shirts and pizza. They’ve been doing creative things with data and algorithms for years by now. So the next logical step is to take off the leash, and let the algos run. Stick a brain on that sucker and see what happens. Let them consume data you can’t even understand, and make hundreds of decisions that don’t make any sense, each second, on your real money. I’m sure it’ll turn out just fine.

    It was never about performance

    Here’s the thing though. None of that applies to regular people. None of it. Regular people don’t need to beat the market. In fact, most shouldn’t even invest. There, I said it.

    Here’s an example.

    Does Average Joe really need to roll the dice to get 15% annual returns on his $500 of savings? He could lose everything, or gain $75. Worth it..?

    Instead, we could just help Joe save $500 each month off his salary, by optimizing his spending and putting him on a savings plan. Zero risk, for a “return” of 1,100% over the same year. Ah. Mazing.

    Which is better for Joe?

    Spender to Saver to Investor

    This is what drives me nuts about -Advisors. They’re supposed to make wealth and advice accessible to the masses, by offering something simple on your smartphone for a minimal fee. It was never about outperforming the market! Joe doesn’t need your fake A.I. or your risky algo strategy!

    Im calling B.S. on A.I. fintech

    Robo should be about financial inclusion. Making wealth accessible. To everyone. Here’s the tagline of Singapore based Bambu:

    We turn Spenders into Savers into Investors

    Don’t assume people need complex investment products. Regular people need to spend less at Starbucks, and save for a rainy day. School fees. A home. Retirement. Those are real things Joe needs. 

    , is Commercial Director at Tigerspike and this article was originally published on linkedin.

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