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October 18, 2017

How AI Affects Financial Services

Summary:

Artificial intelligence can be easily developed and applied in financial service because the barriers to entry are lower with respect to other sectors.

Photo Courtesy of Pixabay

Artificial intelligence is using structured and unstructured data in financial services to improve the customer experience and engagement, to detect outliers and anomalies, to increase revenues, reduce costs, find predictability in patterns and increase forecasts’ reliability…but it is not so in any other industry? We all know this story, right? So what is really peculiar about AI in financial services?

First of all, FS is an industry full of data. You might expect this data to be concentrated in big financial institutions’ hands, but most is actually public, and, thanks to the new EU payment directive (PSD2), larger datasets are available to smaller players, as well. AI can then be easily developed and applied because the barriers to entry are lower with respect to other sectors.

Second, many of the underlying processes can be relatively easier to be automatized, while many others can be improved by either brute force computation or speed. And historically FS is one of the sectors that needed this type of innovation the most, is incredibly competitive and is always looking for some new source of ROI. Bottom line: The marginal impact of AI is greater than in other sectors.

Third, the transfer of wealth across different generations makes the field really fertile for AI. AI needs (a lot of) innovative data and above all feedback to improve, and millennials are not only happy to use AI but to provide feedback and apparently are even less concerned about privacy and giving away their data.

See also: Strategist’s Guide to Artificial Intelligence

There are also, of course, a series of specific challenges for AI in the financial sector that limit a smooth and rapid implementation: legacy systems that do not talk to each other; data silos; poor data quality control; lack of expertise; lack of management vision; lack of cultural mindset to adopt this technology.  

So what is missing now is only having an overview of the AI fintech landscape. There are also plenty of maps and classification of AI fintech startups out there (probably the best ones are this and this), so I am not introducing anything new here but rather simply giving you my personal framework:

  • Financial Wellness: This category is about making the end-client life better and easier, and it includes personalized financial services; credit scoring; automated financial advisers and planners that assist the users in making financial decisions (robo-adviser, virtual assistants, and chatbots); smart wallets that coach users differently based on their habits and needs. Examples include [robo-advisors and conversational interfaces] KasistoTrimPennyCleoAcornsFingeniusWealthfrontSigFigBettermentLearnVestJemstep; [credit scoring] AireTypeScoreCreditVidyaZestFinanceApplied Data Finance;Wecash;
  • Blockchain: I think that, given the importance of this instrument, it deserves a separate category regardless of the specific application it is being used for (which may be payments, compliance, trading, etc.). Examples include: EuklidPaxosRippleDigital Asset;
  • Financial Security: This can be divided into identification (payment security and physical identification — biometrics and KYC) and detection (looking for fraudulent and abnormal financial behavior — AML and fraud detection). Examples include: EyeVerifyBionymFaceFirstOnfido; and FeedzaiKountAPEX Analytics;
  • Money Transfer: this category includes payments, peer-to-peer lending; and debt collection. Examples include: TrueAccordLendUpKabbage;LendingClub;
  • Capital Markets: This is a big section, and I tend to divide it into five main subsections:

i) Trading (either algotrading or trading/exchange platforms). Examples include: EuclideanQuantesteinRenaissance TechnologiesWalnut Algorithms; EmmaAI; AidyiaBinatixKimerick TechnologiesPit.aiSentient Technologies;TickermachineWalnut AlgorithmClone AlgoAlgorizAlpacaPortfolio123Sigopt;

ii) Do-It-Yourself Funds (either crowdsource funds or home-trading). Examples include: SentifiNumeraiQuantopianQuantiacsQuantConnect;Inovance;

iii) Markets Intelligence (information extraction or insights generation). Examples include: Indico Data SolutionsAcuity TradingLucena ResearchDataminrAlphasenseKensho TechnologiesAylienI Know FirstAlpha ModusArtQuant;

iv) Alternative Data (most of the alternative data applications are in capital markets rather than broader financial sector so it makes sense to put it here). Examples include: Cape AnalyticsMetabiotaEagle Alpha;

v) Risk Management (this section is more a residual subcategory because most of the time startups in this group fall within other groups as well). Examples include: AblemarketsFinancial Network Analysis.

See also: Innovation Maturing Into Major Impacts  

If you want to read the entire article, please check that out on Medium.

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About the Author

Francesco Corea is a complexity scientist and AI technologist. Corea is an editor at Cyber Tales and is a strong supporter of an interdisciplinary research approach. He wants to foster the interaction of different sciences in order to bring to light hidden connections. Corea is a former Anthemis Fellow, IPAM Fellow, and he is getting his PhD at LUISS University.

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