AI Trends in the Financial Sector. Microsoft Future Decoded Conference 6 th November 2018 Budapest

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1 AI Trends in the Financial Sector Microsoft Future Decoded Conference 6 th November 2018 Budapest

2 A number of disruptive dynamics are shaping banking... Lending becoming unbundled and moving to the Point of Sale Bank and network facilitated payments are moving peer-to-peer Open data mandates are driving delayering and disintermediation of banking Adjacent players are building eco-systems and intruding on incumbent space Traditional marketing is increasingly replaced by digital targeting Millennials are behaving very differently and having a different view on value Simplicity and experience are emerging as value propositions in themselves Relationship focus is moving from transactions to insight-based interactions Advances in AI, robotics and digital enable radically different cost structures This is driving significant shifts in business models and requires fundamental capability, operating model and technology transformation 2

3 ... and a number of changes and improvements make this dynamics possible shifting the industry into an Open Banking world Technology innovations Regulatory pressure Customer expectations Competitive dynamics New forms of collaboration New technologies are enabling radical step-change business models Artificial intelligence is e.g. improving personalization and enabling messaging as an interface Regulation (i.e., PSD2) is pushing for more transparency and forcing banks to open up their infrastructure for 3rd party developers Increased importance of customer (data) protection, e.g., GDPR Consumers demand experience to be convenient, personalized, transparent, and available anytime and anywhere Merchants and corporates are searching for more integrated solutions Banks are exploring different variations of Open Banking Digital attackers including FinTechs, Born Digital Banks and players from Social & General Tech are accelerating efforts fueled by high VC investments Bank-bank partnerships, e.g., recent Nordic infrastructure collaboration, bank sector solutions in digital payments Bank-fintech partnerships Bank-tech partnerships Fragmentation of banking value chain Tighter integration of financial services into broader ecosystems New players and new business models 3

4 Channels Banks will need to choose what to be in the Open Banking world Models relevant for a major bank Models relevant for a minor bank, startup or niche player Model relevant for very specific conditions Business models Products Viability Own Both own and 3rd party 3rd party 1 Traditional banks offer principally own products through own channels Own 1 Traditional bank 4 Solution provider 5 Aggregator / solution manager 2 3 All things to all people model exposes own products most broadly, but forces to compete on channels Flow monster: highly efficient, lowoverhead producer of standardized high-volume products Both own and 3rd party 2 All things to all people Ecosystem orchestrator Solution provider create end-toend solutions by combining best-ofbreed products, own or 3rd party Aggregators and solution managers create transparency over providers and promote competition 3rd party 3 Flow monster Reseller or service provider Ecosystem orchestrators steer and facilitate collaboration and competition of multiple participants Reseller or service provider serve other providers or parties other than end-users (e.g. on data/analytics) 4

5 Technology is becoming a core differentiator, independent of strategic path Key trends Implications Focus of this presentation Technology innovation investments IoT, connectivity and E2E digital experiences Data capacity and advanced analytics Artificial intelligence (AI) Distributed ledger (blockchain) Enables increased technological consumer innovation and thus a constant inflow of new opportunities especially in a country with consumers being fast adopters (internet banking/ shopping, and smartphone penetration among highest in Europe) Enables new customer experiences, e.g., payment solutions Enables integration between devices, and integration in steps in customer journeys Creates customer expectations of seamless integration between devices in customer journey Enables banks to penetrate value pools outside banking, and technological players etc. to penetrate value pools in banking Enables: AI can be defined as ability for machines to learn and/or problem solve. That enables new and faster ways of engagement, e.g., with improved: Improved product development, e.g., enhanced credit/risk scoring for loan products Increased customization and personalization, e.g., in apps and product offerings Opportunity to do advisory for retail, SMEs and corporates, e.g., on spending habits Ability for interoperability between and solutions providers Sales and marketing, e.g., understanding of customers, with ability to be proactive or precognitive, instead of reactive Risk management, e.g., aiding fraud detection and credit decisions by learning Operations, e.g., bots and digital touch-points, enabling high quality support around the clock Has enabled data secure transactions and agreements, without risk of data conflicts Could potentially change the infrastructure for payments Could potentially disrupt role of middle-men in transactions, e.g., trade-finance Limited short-term impact Technology will become increasingly important independent of strategic path In an open banking world technology will enable and augment new possibilities both from a defensive and attacking point of view Based Based on on strategic strategic choices, choices, the the bank bank should should build build relevant technological relevant capabilities, technological e.g., in advanced capabilities, analytics e.g., in advanced analytics 5

6 In the financial sector AI mainly plays a role in improving marketing and sales, risk management and operations AI in banking Improve marketing and sales Assist with risk management Perform operations efficiently Handle online or mobile customer services enquiries and tasks Improve call center experience Reduce churn by improving relationship Optimize product offering through personalization Asses credit risk faster and more accurately by improved underwriting, advanced early warning systems and personalized collection Improve Operational Risk management Optimize service delivery Monitor the compliance to be followed Use better methods for recruitment and talent retention 6

7 1 The use of AI can be seen across all channels to enhance marketing and sales A B C D Online, mobile and call centers Relationship Management Personalize product offering Provide financial Advisory Description i AI-enabled chatbots to provide customer support over written chats ii Virtual agents to provide E2E services (e.g., bill payment) and customer support in online and mobile banking using natural language processing i Identification of insights from customer data to enable relationship managers to build superior customer relations ii Analyzing behavior of mortgagees or cardholders to identify customers who may be at risk of switching i Identify next best offer for each customer, learning from the customer feedback loop and dynamically price the offer after analyzing real-time customer behavior ii Identify client opportunities at a product / service level and segmenting customers to generate personalized campaigns iii Place the correct products and ads on web applications by learning from customer browsing behavior to upsell or cross-sell banking products i Provide robo-advisory to traders or bank customers after learning from customer and market data and offer an automated trading tool which takes investment decision on behalf of customer using robo-advisory 7

8 2 AI-enabled technology helps institutions manage risk better A B Credit risk management Operations risk management Description i Generating automated credit scores for customers based on structured and unstructured data and making suggestions for improvement ii Making automated credit decisions on amount, rate and duration based on customer banking history and credit scores iii Machine learning algorithms continuously assess the optimal credit line, and produce accurate and timely data for an early warning system iv Collection with an engine, which identifies the right offer, at the right time with the right channel, and granular tracking of results to inform daily huddles i Reduce detection of false positives during identification of fraudulent activities by analyzing and learning from patterns in workflow, transactions, customers, etc. ii For onboarding deep neural networks examine all available data (including images of signatures) and the highest risk applicants are flagged for manual review iii Algorithms detect outliers using machine learning and unstructured data sources and Industry benchmarks identify behaviors that deviate from norms 8

9 3 AI can help in fast-tracking operations in banks A Optimize service delivery Description i Automatically extracting structured information from unstructured machinereadable documents using natural language processing (NLP) to complete KYC or other bank formalities ii Reduce waiting times for services by dynamically identifying bottleneck areas and automatically actioning improvements to create an effective delivery system B Compliance monitoring i Leverage AI to quickly identify and comply with regulatory changes in real-time ii Reducing monitoring inefficiencies for bank s KYC/AML activities by analysing data from millions of transactions C Recruiting and talent retention i Identifying candidate for recruitment by analyzing corporate fingerprint and personality traits outside of the candidate s resume ii Analyzing employee behavior to identify employees who are at risk of mobility and providing effective incentives for retention 9

10 The largest banks in the world have been implementing AI use cases mostly to improve mobile and online experience >80% of the largest banks in the world are using AI-capabilities to improve E2E mobile and online experiences with virtual agents being a major use case 50-80% of the largest banks are using AI-capabilities to improve existing products, improve call center experience, detect fraud and perform recruitments with chatbots being widely implemented <50% of the largest banks in the world are implementing other AI use cases 10

11 Institutions need to decide how to deliver the new capabilities for AI depending on strategic priority, complexity and time to market A B C D Build Tech in-house Acquire Content Partnership Joint Venture Description Building the capability in-house Dedicating existing team or hire new team with capabilities to build AI-enabled solutions Identify the use-case and FinTech Buy the FinTech after due-diligence Buy the products/services as per the business model of the economy Create a new company with stakes from the bank and the FinTech Best for Time to market is not sensitive Capability is core and unique selling point for bank Complex-to-build services Capability is critical for value proposition Quick implementation Capability non-core to value proposition Time to market is sensitive Another player owns a key piece of solution Capability is core and unique selling point for bank 11

12 Fintech partnerships are the most common action banks are taking in their digital transformations % of leading banks 1 that have initiatives in a certain category Bank-Fintech partnership 2 Business accelerator Non-bank program 3 partnership 4 VC/PE 5 Separate digital bank 6 79% 58% 54% 44% 23% 37% Payments Operations 6% 7% Bank-Fintech partnerships 18% Savings and Investment 7 Lending and Financing 16% 16% Customer Service and Care Risk 1 Based on a research covering publicly announced partnerships of the top 100 banks and other digitally advanced banks; 2 Partnering with Fintechs to improve current capabilities and product offering, and enhance customer experience; 3 Program to provide early-stage businesses with time, space, expertise and funding (around 10% of equity) to gain immediate insights from an externally developed idea; 4 Partnering with non-banking sector players e.g. telco, e-commerce that provides opportunities for customer relationship marketing and cross selling; 5 Creating a venture capital or private equity fund to invest actively into early stage, promising start-up companies with high potential return; 6 Traditional bank to establish attacker digital bank with selected product- and service offer under a separate brand; building this takes the longest time and many non-publicly announced initiatives are underway, so the 23% number is likely higher; 7 Also includes account management 12

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