“Robo-advisor” technology has been attracting substantial attention and investment. Financial decision-making is increasingly reliant on algorithms applied to wealth management, personal finance management, investment management, risk assessment and other areas of the financial services industry. Rapidly advancing robo-advisors allow analysts to look into the future and continuously trade securities and other assets based on long-term predictions they are able to build using a real-time stream of data and machine learning capabilities. Among the drivers of automated algorithm adoption in financial services are lower costs of customer onboarding, conversion and funding rates, lower account minimums, lower fees, upgrading technology, and demographic advantages. Assets managed by robo-advisors are estimated to increase by 68% annually and reach ~$2.2 trillion in five years. Other estimations suggest that robo-advisors will be managing $8 trillion globally by 2020.

B2B FinTech companies create real opportunities for incumbents to improve their traditional offerings. For example, white label robo-advisors can improve the customer experience of an independent financial advisor by providing software that helps clients to better navigate the investment world. In the insurance industry, a telematics technology provider can help insurers track risks and drive habits together by providing additional services such as pay-as-you-go solutions.

In investment management, “robo-advisors” have begun to automate wealth advisory roles, calling into question face-to-face meetings and proprietary distribution channels. Robo-advisor algorithms can help with profitability by allowing the relationship manager to spend more time with clients, and less time making portfolios for individuals. LendingRobot is an automated investment service for online lending that uses “high-speed automation software and machine-learning algorithms”. It is available on the two leading P2P platforms, Lending Club and Prosper. The service helps investors automate the lending process, based on preselected criteria, so that LendingRobot is able to select and invest in loans less than one second after they become available. In addition to the speed advantage that automation affords, LendingRobot supports more than 40 different filtering criteria for Lending Club and Prosper Marketplace, and  harnesses machine-learning/artificial intelligence algorithms to help select investments. With this capability, LendingRobot can take into account how quickly and how much other investors have invested in a particular note.

To get ahead of the competition, Personal Capital has signed up 900,000 people for a free dashboard that tracks all your investing, credit and bank accounts; sends daily reports of your transactions and account changes; analyses your asset allocation and the cost of your mutual funds; and helps you gauge your retirement prospects. It then uses that data to prospect for clients for it hybrid financial advisory service it sells for 0.89% of assets a year.

An Atlanta-based company called iAllocate is using artificial intelligence to suggest where you should invest your savings. Currently, iAllocate  provides investors of all levels a DIY set of tools that enable its users to develop a  personal and tactical  Asset Allocation Strategy. The user/investor can then instantly build a diversified Tactical Investment Portfolio with ETF (Exchange Traded Funds) recommendations, which have been intelligently optimized using a proprietary iA algorithm from a selection of more than 400 different ETFs.

Another intriguing option is hybrid services combining robo asset allocation with human financial advisors who offer advice on saving for retirement, financing college, insurance, and estate and tax planning.

In other industry segments, insurance companies are investing in the design and implementation of more self-directed services for both customer acquisition and customer servicing. Customer-centric experiences (e.g. quotes obtained by sending a quick picture of the drivers licence and the car vehicle identification number (VIN)) are gaining popularity.  Some new solutions to mobilize core processes in a matter of hours (e.g. provide access to services by using “robots” to create a mobile layer on top of legacy systems) or augment current key processes (e.g. FNOL3 notification, which includes differentiated mobile experiences) are easing up people to insurance services.

AdviceRobo solutions make use of a machine learning platform that combines data from structured and unstructured sources to score and predict risk behavior of consumers. AdviceRobo provides insurers with preventive solutions applying big behavioral data and machine learning to generate the best predictions on default, bad debt, prepayments, and customer churn. Predictions are actionable because they are on an individual level.

With the pace of improvement that AI, machine learning, and overall technology goes through, robo-advice has the potential to become highly personalized and specific over time, meeting particular needs of different groups. Algorithms don’t have an affluence towards a particular task like fund allocation; the very idea here is that automated advice can get to the point where it can be tailored to analyse any stream of data by demand and become a highly personalized personal assistant in anything. A range of institutions are already investing in the exploration of big data analytics, machine learning and AI application across industries: in customer acquisition, marketing, customer retention, loyalty programs, risk management, etc. In the example of marketing and customer retention, analytic solutions that combine historical transactional data coupled with external information sources can boost the overall conversion rate. Firms are effectively leveraging these solutions to increase the cross-sell and upsell opportunities, understanding customer requirements and providing customized packaging. Card-linked offers and customized reward solutions are some of the offerings that are being provided by financial technology firms.

Robo-advising is not a proprietary breakthrough for investment management, it is a chance for a range of industries to leverage the power of machines in order to jump to the next level of customer service.



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