Fintech Belgium Summit 2018: AI & Machine Learning- Intro Deloitte
•Télécharger en tant que PPTX, PDF•
1 j'aime•682 vues
by Jean M Boxus
Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (18)
Similaire à Fintech Belgium Summit 2018: AI & Machine Learning- Intro Deloitte
Similaire à Fintech Belgium Summit 2018: AI & Machine Learning- Intro Deloitte (20)
Plus de FinTech Belgium
Plus de FinTech Belgium (20)
Dernier
Dernier (20)
Fintech Belgium Summit 2018: AI & Machine Learning- Intro Deloitte
- 1. Headline Verdana Bold The New Physics of Financial Services Understanding how artificial intelligence is transforming the financial ecosystem
- 2. The New Physics of Financial Services 2 © 2018 Deloitte Belgium Unlike past “AI Springs”, AI is poised to continue an unprecedented run of advancement Far from the early premises, AI now benefits from technology advancement, talent, focus and leadership and investment 1950s… 1990s… 2000s… Today and tomorrow
- 3. The New Physics of Financial Services 3 © 2018 Deloitte Belgium So what do people really mean when they say artificial intelligence? A suite of technologies that have made dramatic advances in our ability to automate and enhance: Many applications of AI-driven technology use a combination of the above automations and enhancements Interaction Decision-making Customization Foresight Pattern detection That will be intertwined with the development of all other technological innovations
- 4. 4 AI is changing the physics of financial services…. weakening the bonds that have historically held together financial institutions, while creating new centers of gravity where new and old capabilities are being combined in unexpected ways
- 5. The New Physics of Financial Services 5 © 2018 Deloitte Belgium In the past, dominant institutions were built on… AI is altering the attributes necessary to build a successful business in Financial Services Legacy business models are under pressure from those whose businesses are built around these new attributes Scale of dataScale of assets Mass Production Tailored experiences Exclusivity of relationships Optimization and matching High switching costs High retention benefits Value of augmented performanceDependence on human ingenuity In the future, institutions will be built on…
- 6. The New Physics of Financial Services 6 © 2018 Deloitte Belgium Key findings: AI will transform the financial ecosystem in the following ways Making front- and back-office operations look radically different A new battlefield for customer loyalty From cost center to profit center Self-driving finance Collective solutions for shared problems
- 7. The New Physics of Financial Services 7 © 2018 Deloitte Belgium Key findings: AI will transform the financial ecosystem in the following ways Altering the structure and regulation of financial markets Bifurcation of market structure The power of data regulators Uneasy data alliances
- 8. The New Physics of Financial Services 8 © 2018 Deloitte Belgium Key findings: AI will transform the financial ecosystem in the following ways Raising critical challenges for society to resolve Finding a balanced approach to talent New ethical dilemmas
- 9. The New Physics of Financial Services 9 © 2018 Deloitte Belgium Key take-away Be first and best in the deployment of AI Collaborate with many stakeholders The growing role of AI in shaping the future of financial services will require financial institutions to simultaneously… Responding to the new physics of financial services will require institutions to maintain a challenging balance between competition and collaboration
- 10. Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited (“DTTL”), its global network of member firms, and their related entities. DTTL (also referred to as “Deloitte Global”) and each of its member firms are legally separate and independent entities. DTTL does not provide services to clients. Please see http://www.deloitte.com/about to learn more. This communication is for internal distribution and use only among personnel of Deloitte Touche Tohmatsu Limited, its member firms and their related entities (collectively, the “Deloitte network”). None of the Deloitte network shall be responsible for any loss whatsoever sustained by any person who relies on this communication. © 2018. For information, contact Deloitte Touche Tohmatsu Limited. Jean-Marc Boxus Senior Director Deloitte Consulting | Technology Strategy & Architecture Mobile: + 32 477 59 71 86 jboxus@deloitte.com | www.deloitte.be
Notes de l'éditeur
- From the work of Alan Turing in the 50ies laying the ground for robotics and AI to mainstream success (DeepBlue in chess, Go victory or even poker today), AI has gone through peaks and drought over the last 70 years. However, the view today is that it is here to grow at an unprecedented rate with technology solutions delivery capacity unseen before, a growing pool of talent and also strong investment from large institutions betting heavily on AI and aiming at positive ROI.
- There is no clear agreement on what AI is but it is also clear that people are meaning something when they refer to “AI” When business talks about AI, they are talking about a set of capabilities that allows them to run their business in a new way Pattern detection Recognize (ir)regularities in data Foresight Determine the probability of future events Customization Generate rules from specific profiles and apply general data to optimize outcomes Decision-making Generate rules from general data and apply specific profiles against those rules Interaction Communicate with humans through digital or analogue mediums
- Today, one can easily find white papers and reports on the emergence of AI use cases And technical foundations are also well documented to help you implement and optimize AI solutions But how will this technology change the shape and structure of financial institutions and the competitive nature of financial markets?
- Scale of assets Economies of scale presented a cost advantage Scale of data As AI drives operational efficiency, economies of scale alone will not sustain cost advantages Mass production Physical footprint and standardized products drove cost-effective revenue growth Tailored experiences AI allows the scaled distribution of highly customized products and personalized interactions Exclusivity of relationships direct access to many markets and connections to investors was a critical differentiator Optimization and matching Connections are digitized, increasing the importance of optimizing the best fit between parties High switching costs High barriers to switching providers drove customer retention High retention benefits Continuously improving product performance to offer superior customer outcomes and new value will keep clients engaged Dependence on human ingenuity Processes scaled through additional labour and functional training => Value of augmented performance The interplay of strengths across technology and talent amplifies performance
- Cost to Profit center: turn AI-enabled operations into external services, both accelerating the rate at which these capabilities improve and compelling others to become consumers of those capabilities to avoid falling behind Customer Loyalty: opportunity for institutions to escape a “race to the bottom” in price competition by introducing new ways to distinguish themselves to customers Self-driving finance: Future customer experiences will be centered on AI, which automates much of customers’ financial lives and improves their financial outcomes Collective solutions for shared problems: Collaborative solutions built on shared datasets will radically increase the accuracy, timeliness and performance of non-competitive functions, creating mutual efficiencies in operations and improving the safety of the financial system
- Bifurcation of market structure: As AI reduces search and comparison costs for customers, firm structures will be pushed to market extremes, amplifying the returns for large-scale players and creating new opportunities for niche and agile innovators Uneasy data alliances: In an ecosystem where every institution is vying for diversity of data, managing partnerships with competitors and potential competitors will be critical, but fraught with strategic and operational risks The power of data regulators: Regulations governing the privacy and portability of data will shape the relative ability of financial and non-financial institutions to deploy AI, thus becoming as important as traditional regulations to the competitive positioning of firms
- Finding a balanced approach to talent: Talent transformation will be the most challenging speed limit on institutions’ implementations of AI, putting at risk the competitive positioning of firms and geographical areas that fail to effectively transition talent alongside technology New ethical dilemnas: AI will necessitate a collaborative re-examination of principles and supervisory techniques to address the ethical grey areas and regulatory uncertainties that reduce institutions’ willingness to adopt more transformative AI capabilities. Safety of the financial markets as well as of the customers and workforce are important questions and remain grey areas that need to be addressed While the potential benefits of AI will be striking, its potential risks to societal and economic well-being are too great to be left unaddressed
- Be first and best in the deployment of AI Because those institutions that are able to establish an early lead in using AI as a competitive differentiator will be rewarded by virtuous feedback cycles that compound their advantages and leave second movers struggling to catch up Collaborate with many stakeholders Because unlocking the full potential of AI requires an extensive network of partnerships and only collective efforts by financial institutions, alongside regulators and the broader public sector, can ensure that the expanded use of AI in finance benefits society as a whole