Month: March 2016

InsurTech Could Help Fix The Decline in Life Insurance

Life Insurance

This is the first of two posts by Amy Radin, a guest author. In this first post, Amy describes the business context and outlines some pointers towards a solution. In the second post, Amy will review some InsurTech ventures targeting this pain point.

No one disputes that life insurance ownership in the US has been on the decline for decades.

The question up for debate is what to do about it.

The emergence of an insurtech sector indicates that entrepreneurs and investors have confidence in upside potential. The hundreds of millions of dollars being poured into technology by carriers is another indicator of bullishness.

However, before investors and legacy insurers pour piles of capital into attempts to capture the opportunity using digital technology, they should reflect upon the root causes of this seemingly unstoppable trend, and prioritize innovations that aims at solving the biggest issues.

The Root Cause of the Decline of Life Insurance

The industry chased the high end and ignored the mass market.

  • Carriers have spent decades moving away from serving the needs of the mass market to meeting the needs of a small, high net worth population.
  • A declining pool of independent agents are chasing bigger policies within this segment and ignoring the mass market.

The industry has, effectively, painted itself into a corner, and is trapped in a business model that, given its own complexity, is difficult to change from within.

How have carriers painted themselves into a corner?

Carriers face what Clayton Christensen termed, in his eponymous 1997 classic, “the innovator’s dilemma.” While continuing to do what they do brings carriers closer to mass-market irrelevance, today’s practices, products processes, and policies don’t change. They deliver near-term financials and maintain alignment with regulatory requirements, but ignore large, unmet market needs.

It’s worth acknowledging how the carriers have ended up in this spiral (particularly the top 20, who, according to A.M Best via Nerdwallet, collectively control over 65% market share).

  • Disbanding of captive agent networks for cost reasons has also meant the loss of a (more) loyal distribution channel. The carriers that used to maintain captive agent networks enjoyed the benefits of a branded channel whose agents were motivated to promote the respective carrier’s products. They chose instead to …
  • Shift to third party distribution, increasing dependency on a channel with less control, and where they face greater risk of commoditization. Placing life insurance products in a broad array of third-party channels, including everything from wealth management firms to brokerages and property/casualty networks, has added complexity and increased emphasis on managing intermediated, non-digital channels. This focus comes at a time when other sectors are accelerating the move to direct, digital selling, aligning with changing demographics, technology trends, and consumer preferences for digital-first, multi-channel relationships.
  • Product cost and complexity has raised the bar to close sales and has increased the focus on a smaller base of the wealthy and ultra-wealthy. With the exception of basic term life, life insurance products can be complex. They can be expensive. And, as a decent level of insurance at a fair premium requires a medical exam including blood and urine sampling, it takes hand holding to get potential policyholders through the purchase process. For the high and ultra-high net worth segments, the benefit of life insurance is often as a tax shelter, not simply to protect loved ones from the catastrophic consequences of unexpected earnings loss. More complexity equals more diversion from the mass market.
  • Intense focus on distribution has come at the expense of connecting with the client. Insurance company executives have long behaved as though the agent is the client – if not in word then effectively in deed. The model perpetuated by the industry delegates the client relationship to the agent. This has its plusses and minuses for the client, and has come back to bite the carriers as they contemplate a digital approach to the marketplace where client data and a branded relationship matter. Carriers certainly do not win fans with clients – overall Net Promoter Score ratings for the insurance sector broadly are even lower than Congress’ approval ratings, and for at least one major carrier are reportedly negative.
  • The number of licensed agents is on the decline. The average age of an insurance agent or broker has steadily increased from 37 years in 1983 and is now 59, based on McKinsey Agents have a poor survival rate: only 15% of agents who start on the independent agent career path are still in the game four years later. Base salary is negligible and it’s an “eat what you kill” business. This is a tough, impractical career path for most, and has become less attractive over time.
  • The industry is legendarily slow and risk averse. Think about actuaries, the function that anchors the business model of Insurance. Actuaries make a living by looking backwards and surfacing what can go wrong – a valid role, but the antithesis of what it takes to build a culture where innovation can thrive.

What is the path to opportunity?

Here are innovation thought-starters to create value for an industry undergoing transformation:

  • Clients must be at the center of strategy. Twentieth-century carrier strategy may have been grounded in creating distribution advantage and pushing product, but twenty-first century success will come to those who put the client at the center of all aspects of execution. “Client centricity” is a way of operating a business, not a slogan.
  • Innovation starts with a new answer to the question, “who is the customer.” The agent is a valuable partner, but s/he is not the client. There is white space in the mass market – the middle class – that is not being served by the current system beyond a limited offering. Life insurance ownership has been linked to the stability of the middle class. We should all be concerned with the decline in life insurance ownership and lack of attention paid to this segment.
  • The orthodoxy, “insurance is sold not bought,” creates a self-inflicted set of limitations that can and should be disrupted. The existing product set may have to be pushed to clients because of its complexity, pricing, target audience, channels and near-term performance dependencies.
  • Getting the economics right and meeting the needs of today’s clients will demand a digital-first offering – from being discoverable via SEO and social on mobile screens, to supporting application processing, self-service, premium payments, document storage and downloads, and connection to licensed reps whenever clients feel that is necessary. It will require full digital enablement of agents to create the right client experience, and favorably impact revenues and expenses. Ask anyone who has purchased life insurance about his or her decision journey, and invariably you will find out that shopping for insurance is a social, multi-channel experience. People ask people whom they like and trust when it comes to making important life event-based decisions. Aligning to how people behave already is a winning approach, and is what customer-centricity is about.
  • In a world of big data, it’s ironic that the insurance sector is one of the most sophisticated in its historical use of data, yet is late to the game leveraging big data. Winners will realize the potential of new data sources, unstructured data, artificial intelligence and the many other manifestations of big data to personalize underwriting, anticipate client needs, create efficiencies, and deliver via positive experiences including multi-channel distribution and servicing. Amazon, Apple and Google have set the standard on what is possible in customer experience, and no one will be exempt from that standard.
  • Life insurance products may be infrequent purchases, but the need to protect one’s loved ones is daily. In today’s product-push model, an ongoing relationship beyond the annual policy renewal, or a claim, is the exception. Consider the potential of prevention services as a means of boosting lifetime value and client loyalty. In a world full of insecurity, there is a role for an ongoing conversation about prevention and protection. But, the conversation must be reimagined beyond pushing the next product to one that revolves around serving the client.

Amy Radin connects customers to companies to create growth. She brings an unexpected combination of insight, reinvention and pragmatism to companies in transformation. Amy serves on Advisory Boards, is an angel investor, keynote speaker, and workshop facilitator. She consults with companies from startups to Fortune 500 applying her Framework for New Growth (c) to help companies attract new clients and expand client relationships.

 

 

Is online financing the death of the credit card?

shopping

Instant online financing at the point of sale could turn out to be big business for the small end of town, especially if companies like Afterpay, zipMoney and Affirm gain further traction. And for a generation yet to be weaned onto credit cards, it could mean a shift in personal financing trends away from the trusty plastic card and towards more flexible and ‘cheaper’ alternative providers.

Retail financing is certainly nothing new – department stores and larger retailers have been doing it for years. Only in the past, it often required cumbersome paperwork, a sales person tied up for 20 extra minutes or, more frequently, a requirement by the customer to sign up for yet another branded store credit or charge card. But what about those customers who don’t step foot inside a physical shop? Enter online financing.

Affirm

Affirm is the big player in the US, and is currently headed up by Max Levchin, co-founder of PayPal. Integrating with online checkouts, the platform allows customers to take out loans directly at the point of purchase, reducing cart abandonment and increasing basket size. Of course, there are no free lunches in finance, and someone has to pay. In Affirm’s case it’s the user, copping between 10 and 30 percent APRs over the lifetime of the loan.

zipMoney

zipMoney, which listed on the Australian Stock Market (ASX) via backdoor listing last September, offers a similar product to Affirm. Claiming to have over 150 online merchants on its books, it’s data shows merchants on average see a 50 per cent increase in basket spend and a 20 per cent overall increase in sales. Not a bad result in an increasingly competitive online retailing market. Users can expect a honeymoon interest free period followed by, well, we’re not quite sure. The website is rather non-transparent on its APRs, leaving customers to no doubt sift through terms and conditions once they’ve signed up. If they ever do, that is.

Afterpay

Afterpay has taken a slightly different approach to the local Australian online financing market, choosing instead to only charge the customer the advertised purchase price, broken down into 4 monthly instalments. Instead, the retailer pays for the cost of the financing. The company intends to list soon on the ASX, issuing 25 million shares with an offering price of $1.00 per share. For those keen to get a slice of the online financing market in Australia, Afterpay’s prospectus can be accessed here. Given zipMoney is currently trading at around the $0.40 mark, it may be interesting to evaluate the companies side by side.

Online financing has its detractors, with many arguing it’s just another easy debt trap for the less well-heeled. Interestingly, a quick search of a few forums suggested those who had applied for finance through more traditional means and were declined, often found they were accepted via an online financing provider. There’s nothing wrong with this, one just hopes, for investors sakes, that online financing outfits know how to price that risk correctly. Either way, for small business to compete against their larger competitors, online financing will no doubt soon become a ‘must-have’ check-out addition, in-store and online.

Daily Fintech Advisers provides strategic consulting to organizations with business and investment interests in Fintech. Jessica Ellerm is a thought leader specializing in Small Business.

Mobile wallet interoperability

mobile-wallet

Mobile wallets could finally break the dominance of Visa and Mastercard by evolving from P2P to C2M (Consumer to Merchant) and evolving from small transactions to larger transactions. That’s the good news. The bad news is that if network effects rule – and they usually do – we might miss the credit card networks as we end up dealing with one or two behemoths that controls both cash and credit. Or we could all have mobile wallets that work with every other mobile wallet. That is how physical wallets work.  There is a lot at stake in the geeky subject of Mobile wallet interoperability. 

Would Sir like to pay with Apple or Google?

In the world where one or two behemoths rule, our wallets will be determined by our mobile phone operating system. Merchants will accept both Apple and Android like they accept Visa & Mastercard today.

That is one scenario. It is unlikely because the other Big Techs – Facebook, Amazon, Alibaba – won’t sit still for dominance over something as critical as payments, let alone Visa & MasterCard and then anti trust regulators will jump in…

It also looks like Apple Pay is not taking over in the way some people predicted at launch. According to pymnts.com, the number of iPhone 6 consumers in a store where they could use Apple Pay which actually did use it dropped from 48% in March to 33% in June last year.

That makes sense. The merchant has to have an NFC terminal and the consumer has only a marginally better experience. Innovation happens when something is 10x better or there is no alternative. Minor changes don’t change consumer behavior unless friction is close to zero.

I can send text messages and emails to anybody, no matter what phone they have and what carrier they use. Sending cash should be as easy. It should be as easy as physical cash with the huge advantage of location independence. That is the vision of mobile wallet payments. Which will only happen if we get interoperability.

How could we get Mobile wallet interoperability?

There 7 ways this can happen:

  1. Central Bank controls and sets the standard. Check out Ecuador Dinero Electronico to see how this can work. Our take: unlikely to go beyond an interesting footnote unless Federal Reserve or ECB does this, which is unlikely.
  1. M-Pesa. Despite massive traction in markets where Vodafone has big market share, it is unlikely to become a global standard because governments and Big Tech will resist dominance by a single carrier.
  1. Network effects create de facto standard. One can see a battle for market share in India with companies like Paytm doing well. One can see iZettle winning in Sweden even if today they are more credit card oriented.  One can envisage one company getting dominance in one big market like India or Sweden and then moving into adjacent markets, but on a global basis this seems like a big stretch.
  1. Bitcoin. This hits a lot of high points – permissionless, open, global. But it fails by being a currency that threatens government, thus creating an on ramp and off ramp problem that gets in the way of a frictionless offering. A wallet has to be currency agnostic.
  1. Committee designed standard. These initiatives are worthy but they almost never win in the market. Consumers don’t care and BigTech play lip service only. Unless the standard already has traction with consumers it tends to remain a paper tiger.
  1. Low level standard. USSD is the one to watch. However it does need higher-level protocols to be agreed as well and it is GSM only.
  1. Some mix of carrier plus open standard. What Orange Money is doing is interesting. They have other carriers on board as well. This could be like an open version of MPesa.

Credit & larger transactions will come later

Mobile wallets are getting traction in two types of markets, at opposite ends of the maturity spectrum:

  • In underdeveloped markets where ATM and Credit Card penetration is weak. This is shown by the success of Paytm in India and MPesa in Africa.
  • In highly developed economies where people want to get rid of cash. Sweden is the country to watch.

Mobile wallets today are only cash and tend to be for small amounts. They enable somebody without a credit card to pay or they enable somebody to pay their barista without messing around with grubby notes and coins.

This still leaves two big use cases:

  • When you need credit. If mobile wallets become ubiquitous (which is quite feasible as 50% of the global population have mobile phones and the growth rates don’t show any signs of slowing down), all it takes is an entrepreneur to figure out how to offer just in time credit. Today, credit cards conflate payment and credit. If the payment part moves to mobile wallets, it becomes feasible to add the credit part as a separate service.
  • For larger transactions. It is feasible to link the mobile wallet to your bank account so that you can see balance and do transfer as needed. You look in your wallet. You see $10 in there. You spend $3 on a coffee. Then you want lunch and it costs $15. You look through your wallet to see $450 in your checking/current account with a notice saying “2 days to payday” so you download $100 into cash in your mobile wallet and pay $15 for lunch.

The point is not to minimize the challenges of just in time credit or just in time balance transfer. However they are technically feasible today and if we get mobile wallet interoperability, they will become commercially feasible.

Daily Fintech Advisers provide strategic consulting to organizations with business and investment interests in Fintech. Bernard Lunn is a Fintech thought-leader.

 

 

The agile ‘just in time’ AI powered CFO

KCIU8RWM09

According to an EY report, the average age globally for a newly minted CFO is 42. Unsurprisingly, they’re a highly educated bunch, with 27% of CFO’s surveyed having completed an MBA and 27% holding a chartered accountancy qualification.

Highly educated talent with years of experience usually doesn’t come cheap. And for small to medium businesses, this could mean forking out a salary in the range of $130,000 to $250,000 to land themselves a C-Suite financial executive.

So with CFO’s trading at premium, is there any way the knowledge, analytical brains and strategic insights a CFO possesses could be mass produced for less? For companies turning over between $1M to $5M per year, deep learning, big data and AI is quite possibly the answer.

Medical profession leads the way

We need not look far to see a similar story of knowledge democratisation at play – medicine. In late 2015, computing powerhouse IBM acquired Merge Healthcare, a medical imaging business with a collection of over 30 billion X-rays, CT and MRI scans. These images are ready fodder for it’s hungry AI protégé Watson. IBM plans on using the data, alongside other diagnostic indicators such as pathology results, genetic data and clinical studies, to train Watson to recognise diagnostic patterns and abnormalities.

The impact of these sorts of deep learning initiatives in the medical sector is huge. When presented with a patient in the future, in order to diagnose and treat, Watson will be able to call on a database of knowledge far wider than any medical professional alone. The era of Dr House could soon be eclipsed by that of Dr Watson – and it’s hard to see how patients stand to lose.

The ‘just in time’ CFO

The parallels to financial decision making in the business sector are evident. At any one point in time, to effectively manage an organisations financial health, CFO’s must monitor and then interpret a myriad of data points. Not only must they track internal financial metrics, but they are increasingly required to keep a steady pulse check on the wider financial markets, investor sentiment, and microeconomic indicators pertinent to their sector. With data often fragmented internally and externally – and shifting fast – decision making is a complex undertaking.

For small finance teams, such as those in small businesses, the problem is compounded by a lack of headcount. In many instances, the financials are only looked at in depth once or twice a year by an external accountant. That means, for the rest of the year, business owners are left to their own devices, often unaware or unable to comprehend looming financial mishaps, like cash flow shortages or inventory issues.

But, what if a business owner or lower level finance executive could tap into an online CFO, powered by a Watson engine, and obtain insights and recommendations in a heartbeat? An engine with access to numerous financial data sources in real time – internal and external – crunching the numbers and providing ‘just in time’ answers to company specific operational and strategic financial questions? This flexibility would help small business build financial agility into their operations in an affordable way, without the need to rely on static financial planning sessions or expensive advisors.

Watson powering sales efforts already

A great use case for Watson in the business world can be seen in RedAnt’s SellSmart, an offshoot of the the IBM Watson Ecosystem. Using the cognitive engine, the app addresses the pain point of sales staff struggling to keep up to date on ever increasing swathes of product knowledge. Responding to natural language questions, the app can promptly deliver the product information required for the sales member to assist the customer, improving the in store relationship and increasing the likelihood of closing the sale.

Financial data could be delivered in a similar way. Tomorrow’s small business CFO may very well sit within an iPad, and thanks to cognitive AI, might understand what business owners are trying to ask about the financial well being of their businesses, without them necessarily having to use complex technical financial jargon in order to get there.

CFO Watson might be able to:

  • Analyse sales across online competitors and advise what future inventory should be purchased to capitalise on market trends
  • Forecast and recommend various pricing strategies with respect to their impact on revenue and profit
  • Automatically redistribute cash across 2 or 3 term deposit accounts to maximise interest earned verses working capital on call.
  • Crunch weather patterns and advise the business to renegotiate an order placed with a supplier for summer stock
  • Pick up on consumer sentiment to target marketing spend and sales efforts more effectively

Today the concept of a small business ‘Virtual CFO’ is merely a case of outsourcing financial decision making to an accountant or financial expert. It still doesn’t replace the human at the end of the chain providing the advice and recommended actions after crunching the numbers and interpreting the reports.

Given the financial insights required by small business are significantly less complex than larger entities, utilising AI at the development stage it is at today is feasible. Overly engineered solutions for financial management are not required for this sector. But being able to translate financial data sets, trends and patterns into reliable and actionable insights is.

Daily Fintech Advisers provides strategic consulting to organizations with business and investment interests in Fintech. Jessica Ellerm is a thought leader specializing in Small Business.

On Monday, Daily Fintech will take a holiday for Easter. For the first time since we started in August 2014, there will not be a daily post. Fintech aficionados can still surf our archives of over 400 research notes.  

Could #InsurTech AI machines replace Insurance Actuaries?

This is the fourth in our AI in Fintech Week series. You can see the intro post describing the current state of the art in AI here. Today we look at a job that very few people understand. It is a job that requires an aptitude for math and statistics plus knowledge of complex domains such as life expectancy, healthcare, accidents, weather, wars & terrorism. Fundamentally it is a job that requires math and statistics; our AI friend Hal is heard to say, “I am good at math and statistics, give us a job”. It is possible that the flood of new real time data into Insurance will change the Actuary job beyond recognition or even replace it with AI machines. That is the theory that we explore in this research note.

The Actuary job

Actuaries do statistical modeling to assess risk. However unlike using statistical modeling to assess financial risk, the job is not open to anybody with skills in math, statistics and computers. The Actuary job is a profession with rigorous professional certifications.

This site has the facts.

  • It requires a big investment in education. “It could take from 6-10 years to pass all of the exams, but you can begin a career as an actuary by passing the first two exams, and then taking subsequent exams while working as an actuarial assistant.”
  • It is a well-paid job. “Experienced Fellows have the potential to earn from $150,000 to $250,000 annually, and many actuaries earn more than that.”
  • There are not that many Actuaries. Estimates vary but the number is around 10,000 in the USA. An upper bound would be 3x globally ie about 30,000. That is small compared to other professions/jobs.

Data is the game-changer

There four main domains for Actuaries to model

  • Property & Casualty for Cars.
  • Property & Casualty for Houses.
  • Life Insurance.
  • Health Insurance

The data points, and models are quite different in these 4 domains, but data is the key to all of them and that is where disruption is coming from. The big game-changer in Insurance is the data flowing from new data sources such as telematics in home and in car for Property & Casualty and wearables on the body for Life and Health.

One company to watch is Atidot, which is a classic Israeli startup (founders ex military intelligence, steeped in cyber security, data science and software development).

Atitdot are moving the actuarial process from classic statistical analysis using static models to predictive analytics using dynamic and non-linear models. The real world works on real time, dynamic models. Reality does not obey the dictates of a static model. The problem is that our organizational processes are based on static models.

When will this person die?

That is a classic question that actuaries need to answer, specific to Life. In Health, the question might be “what is the chance of this person getting diabetes?” In Cars it might be ““what is the chance of this person having an accident?” Lets stick with the Life question as it is the simplest. A simple statistical model might have the following variables:

  • Age
  • Sex
  • Job
  • Location
  • Health risks such as tobacco, alcohol and medical diseases such as diabetes.
  • Average life expectancy

Occasionally the model is changed because of something global changes such as average life expectancy. Sometimes you can refine based on changes in risk profile of a job or a location. These changes affect so much in the Insurance business that they need to be carefully considered and go through committees and long established approval workflows.

That is a description of the Static Batch Enterprise, but the world is dynamic and real time.

Atidot applies many more variables simultaneously to process much larger data sets. The new data sources – telematics, wearables, social media, weather, news – are inherently real time and dynamic.

Some other ventures in this space worth tracking are:

Quantemplate focused on Reinsurance

Analyze Re focused on Reinsurance

FitSense focused on wearables data for Life & Health.

Wunelli focused on in-car telematics data for car insurance

Other AI Use Cases in Insurance

There are many possible use cases for AI for Insurance. Many of the use cases cited are really Big Data with some relatively simple algos. These are valuable but do not fall into the reasoning machines that we call AI. There are use cases where a robot replies via SMS to a custom query. These are real AI and they can to pass the Turing test. However they are not specific to insurance and can be applied to all CRM and Support use cases in any industry.

A huge pain point specific to Insurance is changing models related to weather due to climate change.

The company to track here is Meteo Protect.

AI can be used in claims processing to detect fraudulent claims. This is where human experience can detect patterns.

Replace or Augment?

Our thesis is that Actuaries will be augmented rather than replaced. AI machines will do the work that Actuaries do today. However human Actuaries will move up the value chain for two reasons:

  • The Actuary profession is full of smart, well-educated people and many of them are in positions of power. They will adapt to change.
  • There is far more value to an Insurance company from accuracy than cost-cutting. An experienced Actuary empowered by real time data and non-linear models will be hugely valuable. The Actuary defines how much to charge for a Premium and that is as core to an Insurance company as you can get.

Regulation

In some jurisdictions, Insurance companies have to communicate to consumers how rating factors such as gender, age, zip code are used in pricing. That privacy issue will get more complex as we get into in-car telematics, smart home sensors and wearables. It is hard to imagine an Insurance company being allowed to do surge pricing like the transportation business just because the AI machine tells the Insurance company that risk has spiked.

This will be an issue that regulators will have to grapple with as InsurTech startups and Insurance incumbents battle for customers.

 

Daily Fintech Advisers provide strategic consulting to organizations with business and investment interests in Fintech. Bernard Lunn is a Fintech thought-leader.

 

AI in Digital Wealth mgt: Algorithms

ALGO TRADING

Are we looking for an algorithm that “If we all die, it would keep trading”? Should we be worried that electronic trading is mushrooming like airplane traffic, while we are not paying that much attention?

Today, I’ll look for AI pigments of incremental changes in algorithmic trading, first on Wall Street and then outside, in the Fintech startup world. I am not including the HFT space because it is a particular space driven by speed and merits a separate post because of its politically sensitive angle (Michael Lewis’s babe).

Renaissance Tech and Two Sigma, are probably the most recognizable names in old fashioned quant trading space. Bridgewater, publicly announced a year ago that they will start a dedicated AI team reporting to David Ferrucci, who joined Bridgewater 3yrs ago after leading the IBM group that developed Watson. The FT reports that Mr Luo chief quant at Deutsche Bank, leads a team that has developed an AI algorithm that searches the financial system for investment opportunities, “scraping unfathomably large data sets to unearth profitable patterns to proffer clients”.

Early stage startups focused on using advanced quant techniques for trading are mainly focused on machine learning techniques to calibrate models that manage money typically on a discretionary basis.

Walnut Algorithms out of Paris, is applying latest advances in data science and machine learning research to the financial markets. They combine advanced machine learning techniques with financial expertise to generate medium to high frequency pure alpha strategies. Euklid, out of Italy, employs a sophisticated AI engine capable of making independent trading decisions. The AI is based on complex algorithms which are monitoring technical indicators and analyzing market trends. Both are StartupBootcamp Alumni. Euklid has a much broader vision of becoming the first AI bank, offering digital crypto currencies and employing smart contracts.

ArtQuant, out of Moscow, has created a product PortfolioandMe, positioning AI as a service for portfolio advice, monitoring and construction. If the customer chooses the in-house portfolio construction, then the AI algorithms of ArtQuant are used with a fundamental analysis approach.

Similar to a hedge fund approach, Aidyia, from Hong Kong, is taking AI in finance to the next level, acting as an asset manager with a long short equity fund. Aidyia plans to launch more fund products using quant techniques from genomics and robotics.

Another company taking general business intelligence algorithms and applying them to financial trading, is Sentient technologies, a San Francisco company who got more than $100mil funding at the end of 2014. Sentient has been trading with AI systems over the past year (no fund product available yet) operating in the discretionary management space. In last week’s article in BBC news, Would you let a robot invest your hard-earned cash? Sentient AI was mentioned as one of the two companies focused on training algorithms to learn from past mistakes and refine their rules, without the need for much human intervention. New-York based Rebellion Research is the other one, which is offering machine learning asset management. They offer a basic Global Equity strategy, which has been managing money since 2007 via a Hedge Fund structure and as well as in managed accounts. They also offer the possibility of creating a Market neutral strategy; an absolute return strategy and a US bond strategy. These sophisticated strategies are available through managed brokerage accounts at Interactive Brokers.

Another brokerage firm getting involved in AI more directly is  Silicon Markets, a Fin-Tech, that uses MT4 platform and a customized version of it for its brokerage business, Tradable. Silicon markets is launching a Machine Learning Trading Optimizer which will be integrated with Tradable.

Fintechs threating directly professional asset managers, are those also that empower the DIY retail traders and create alpha directly for end users (investors).

Alpha Modus is delivering alpha directly to end-users through a marketplace of investment technologies they call ‘mods.’ These mods are priced according to how much alpha they generate, so investors can buy alpha on this platform without paying the conventional high fees of alpha-generating asset managers. One of the key technologies available in the Alpha Modus marketplace is the Early Look Imbalance Meter, which aims to predict the direction of the NYSE into the closing auction. Recently, Early Look mod has been combined with the IBM Bluemix platform, where they have incorporated Watson’s Insights and Twitter sentiment analysis. A live case study of cloud based collaboration between fintechs and incumbents.

Quantopian of course, belongs in this classification too, since it offers a platform for quant algorithms development, back testing, and also, is creating the first crowd-sourced hedge fund by backing selected investment algorithms.

Capitalico is a trading platform that makes quantitative analysis and algorithmic trading possible for everyone on a smartphone. With no programming skills, the platform allows you to build, test and trade an algorithm out of an idea with a few clicks and based on visuals of historical charts. A powerful tool for DIY traders, out of California. Alpaca is the company behind this platform, been developing Deep-Learning trading algorithms.

Celebrating AI in digital asset &wealth management, continues with ten picks focused on algorithmic trading of all sorts (long-short equity, crowd-sourced hedge fund, mods, etc), to sophisticated enablers of DIY individuals. AI in digital wealth management is also morphing customer preferences and profiles. These incremental services are changing trading and investing by blurring the lines between these activities.

Daily Fintech Advisers provides strategic consulting to organizations with business and investment interests in Fintech. Efi Pylarinou is a Digital Wealth Management thought leader.

 

 

AI in Digital Wealth mgt: Sniffing out investment opportunities

analysts

Economist Andrew McAfee concludes in his TedTalk “What will future jobs look like?” (already 3yrs old) that “The new ‘algorithm enabling’ technology is here today, and banks could use it to fundamentally change the value proposition for their customers.”

Oscar Wilde said that:

“The future belongs to those that can recognize opportunities before they become obvious”.

Artificial intelligence, which encompasses these days all sorts of ‘algorithm enabling’ technology, is creeping into our lives. Asset management and wealth management is no exception. Mentors at Fintech accelerators are advising entrepreneurs to drop the idea of creating the next Bloomberg and are suggesting a focus on AI finance. The big bang isn’t happening yet. It will happen (despite our human predictive unreliability) and I foresee it unfolding incrementally.

Incremental changes in financial technology and in customer preferences & profiles, are going to make-up the new era, the “Second machine age” era.

We celebrate today pigments of these fintech incremental changes because they are shaping our future.

NLP

The financial answering machine, Kensho, which we profiled a year ago in Kensho: Warren is like Watson and Siri, for analysts, investors and traders, is using a natural language based algorithmic technology. It is clearly a threat to financial analysts and to asset managers, whose ability to process and interpret financial information maybe replaced by a machine. Kensho and Aylien based in Dublin, are both faster and can extract value from text by using deep learning techniques. They process news and media info, research and business documents. Palo Alto-based Sensai, also uses natural language processing to help companies analyze unstructured data, such as corporate documents, transcripts and social media.

Sentiment analysis

Focused on sentiment analysis that use NLP and statistics and machine learning, Sentifi, is a Swiss based company that uses crowd sourcing algorithms to provide actionable investment advice to large financial institutions and to financial media companies. They currently offer 4 products: myMarkets; myPublishing, myCompany, myScore.

Amareos from Hong Kong, also contributes to financial decision making based on sentiment analysis (heatmaps and data visualization) with a tilt towards analytics and indicators used in trading systems and risk monitoring.

Combining sentiment analysis from crowd sourced data & quantum encryption, Running Alpha is offering a smarter way for seeing investment opportunities before they get noticed; helping investors be first at exploiting high-impact performance trends with confidence. They offer two sophisticated heat map products classified as Sentiment-Aware Portfolio Solutions: the Grid 100 and Focus 15. A subscription based service for retail and asset managers to generate alpha by shedding light into dark information (dark through a human eye lenses).

Predictive analytics

EidoSearch, a Canadian company using advanced pattern recognition by processing large data sets (crowd sourced info) and producing probabilistic predictive analytics. They recently announced a partnership with Stocktwits, to enhance their idea generation and risk management abilities; bringing to the masses more power to uncover investment opportunities before they are all priced in.

I know First, provides daily investment forecasts based on an advanced, self-learning algorithm. Their predictive analytics are based on Artificial Intelligence and Machine Learning with elements of Artificial Neural Networks and Genetic Algorithms; results are delivered on your mobile; another enhancement for DIY traders.

Clusters in real-time

For professional traders, to protect themselves from dark or speedy events like those hidden in HFT trading, flash cards, and other algorithmic activities; AbleMarkets research that can be used by fund managers, family offices, brokers and exchanges.

Another U.K. startup, AlgoDynamix claims it can warn you before the market undergoes a nasty selloff. It uses real-time data from exchanges, looks for patterns and searches for clusters of traders who are bailing out of an investment. AlgoDynamix provides clear and visual signals when sellers are gaining strength before a broad slide kicks in.

 

This is a taste of the powerful, even though incremental, changes in discovering investment opportunities. They are sourced from algorithmic enabling technologies applied in areas as natural language processing, behavioral finance, sentiment analysis, predictive analytics, pattern recognition. Business models of these Fintechs are mostly subscription based and democratize the space of such technologies that were strictly available to large institutions.

Celebrating AI in digital asset &wealth management, continues. We started with ten picks that are in the business of “sniffing out” in digital wealth management and changing the value proposition of financial analysts and asset managers: Kensho, Aylien, Sensai, Running Alpha, Sentifi, Amareos, EidoSearch, I know First, Ablemarkets, AlgoDynamix.

Daily Fintech Advisers provides strategic consulting to organizations with business and investment interests in Fintech. Efi Pylarinou is a Digital Wealth Management thought leader.