Trends and practical applications of AI/ML in Fin Tech industry - Milos Kosanovic
•Download as PPTX, PDF•
1 like•285 views
The talk will have 3 parts. The overview of the practical applications of the AI and ML in the FinTech industry with a short explanation of the PSD2 directive and the disruption is caused. Application of the AI/ML from the perspective of the end-user, personal financial health, financial coach, etc. The overview of the architecture, technologies, and frameworks used with practical examples from the Zuper company.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Trends and practical applications of AI/ML in Fin Tech industry - Milos Kosanovic
Similar to Trends and practical applications of AI/ML in Fin Tech industry - Milos Kosanovic (20)
More from Institute of Contemporary Sciences
More from Institute of Contemporary Sciences (20)
Recently uploaded
Recently uploaded (20)
Trends and practical applications of AI/ML in Fin Tech industry - Milos Kosanovic
- 1. Trends and Practical Applications of AI/ML in FinTech Industry Miloš Kosanović, Zuper GmbH
- 2. Agenda 1) The History of Banking 2) Open Banking 3) AI in FinTech 4) PFM – Personal Finance Management 5) Practical Application of ML
- 5. 2. Open Banking ▪ Allows access and control of consumer accounts through third-party applications. ▪ Can reshape the competitive landscape and consumer experience ▪ Potential gains and grave risks to consumers as more of their data is shared. • EU open banking regulations: – GDPR – The “right to be forgotten” raises the stakes of data sharing – PSD2 – Payment Services Directive – SEPA - Single Euro Payments Area – SCA – Strong customer authentication
- 6. Global Open Banking Developments
- 7. Market and Trends • Three major groups of providers in the financial system. • New data sources and increased amount of data • Main services: deposits, savings, lending, payments • Digitalization – transforming data to meaningful information • Technology integration – AI, Cloud computing, etc. • First-mover advantage • Disruption - First Principle Design VS Iterative Thinking • Behavioral Science Integration • Ubiquity and Context-Aware Human/Machine Interactions • Collaborative effort (Nordigen, Zuper) • Creating new services • Job loss
- 8. The Market - competitive dimensions • Consumer trust • Technological platform • Customer reach • Regulatory competence and industry know-how
- 9. Fintech100 2018, H2 ventures, KPMG Top-100 FinTech Sector Breakdown
- 10. 3. AI in FinTech • Robo-advisors • AI that executes trades • Fraud and money laundering prevention • Hyper-personalization (KYC) • Alternative credit scoring • Empathetic Financial Assistance and voice recognition • AI for customer prediction and insight
- 11. The impact of AI on business Personalization Optimized Targeting Automated scoring of customers to decrease churn Customized onboarding Marchant analysis & new opportunities Bots that handle user requests Voice / Face identification Fraud & Money laundering detection “Instant” transactions approval Reconciliation & data verification Portfolio optimization via reinforcement learning Cash forecasting and optimal trade execution Alternative credit scoring “Instant” loans Robotic Process Automation Augmented risk modeling Personalized rates INSURANCE DEPOSITS & LENDING INVESTMENTS BANKING & PAYMENTS MARKETING & LOYALTY
- 12. 4. PFM – Personal Finance Management Tool Financial Coaching: Google insights fit for your Financial Health
- 13. I want to keep my money in a place where it will grow. I want access to credit when I need it. I want help to plan my financial journey. I want services in my phone, where I do everything else. Smart visualization of opportunities and benefits in his/her deposit accounts. Automated refinancing offers based on the user’s financial scoring improvements. AI-Driven Coaching, Gamification, Push/Pull Account Alerts, Automated Routines. Instant In- & Cross- Country Payments. Mobile-first helps users access services globally. Personal Finance Management Tool - PFM
- 14. ▪ Recognition of voice commands i.e. Google Assistant ▪ Motivation: Show users how they compare to others ▪ Gamification / Positive Social Pressure ▪ Edge computing: Machine learning models on the mobile device; Real-time and offline predictions. ▪ Predictive Overdraft Protection: Predicting when and by how much a user may overdraft, offering help to prevent it. ▪ Scheduled Transactions: Users know which routine payments are coming up so there aren’t surprises. ▪ Credit Scoring Algorithm: Zuper uses transactional data to develop an internal credit score. AI/ML for a new era of digital wealth management
- 15. The impact of AI on customers Faster Response Integration with virtual Assistants Voice control Credit score monitoring Instant lending Overdraft Prediction & Protection Instant Payments Recurring transactions Automatic payments Transactions Categorization Card fraud prevention Hidden fees detection Budget suggestions Savings suggestions Impulsive buys prevention Contract Recognition Social Pressure Timely, contextual warnings, reminders and opportunities Identification of financial weaknesses Hints and advice Gamification INSIGHTS LOANS & PAYMENTS AUTOMATED ACCOUNTING TECHNOLOGY CONVENIENCE FINANCIAL EDUCATION
- 16. Join Monthly Challenges October Challenges Join Try a “Buy-Nothing” Weekend Stock Your Emergency Fund! 21 Days Left - 3,034 Joined External Benchmarking Feedback for Self-Monitoring You’ve come in below budget two months in a row. Let’s keep your streak rolling! Nice work, Eve! You normally pay 22% more for food and 16% less for rent than the other Zuper users in your age range who live in Berlin! Analysis Learn More Transparency in Credit Scoring Improve Your Score Track Progress Over Time Eve’s Hall of Financial Victories Gamification & Behavioral Science Join
- 17. Examples • Credit scoring • Transaction Categorization • Overdraft prediction • Contract recognition
- 18. Credit scoring • Which words have positive correlation with successful loan repayment? • “God”, “Debt free”, “Thank You”, “Min. payment”, “Promise”
- 19. Technology • ML model types • Rule-based engines • NLP and NER • Neural networks and deep learning • Microservice Architecture • Model Deployment
- 20. Risks and Challenges • Risks of sharing data • Adoption of regulations • Lack of data – ATM, PayPal and credit cards • Emerging use cases • Explainability, transparency and ethics of ML models • Education and coaching • Skills shortage – Finding skilled workers
- 21. Miloš Kosanović WEB: www.getzuper.com TWITTER: @mk0sanovic EMAIL: mk@getzuper.com
Editor's Notes
- I’m a Software Engineer, Ph.D. student, and a Professor of Professional Practice who currently works as a Data Scientist and Machine Learning Engineer at Zuper GmbH. I was born and am currently living in Niš, Serbia. Trends and Practical Applications of AI/ML in FinTech Industry The talk will have 3 parts. The overview of the practical applications of the AI and ML in the FinTech industry with a short explanation of the PSD2 directive and the disruption is caused. Application of the AI/ML from the perspective of the end user, personal financial health, financial coach, etc. The overview of the architecture, technologies, and frameworks used with practical examples from the Zuper company.
- Who in the world enjoyed his banking experience? ☺ Let me tell you that we are living the revolution of banking as we know it. FinTech – Innovative usage of technology that is transforming banking as we know it. Similar to disruption that Uber, and AirBnb made. AI, ML, blockchain, Cloud services, etc.
- Trust and the history of banking, this is why banks are built to look massive and like they will never die Face to face was important because you bleive the person in front of you. Utility - the state of being useful, profitable, or beneficial. Bank 1.0 - The world is changing so the banking has to change. Important elements are TRUST, Bank 2.0 ERMA from MIT in 1953, when technology started to change banking. First time we introduced BANK ACCOUNT number! Bank 3.0 – Banks anywhere and anytime Bank 4.0 – UTILITY and ubiquity Your bank account holds your money, and you probably use your checking account for most payments. But technology increasingly creates options to maximize the value you get from your bank. With open banking, third-parties can help you save money, borrow easily, and pay painlessly. Paper work and long waiting queues
- Who owns your bank data? Transactional data reveals actual spending patterns, solvency, and more accurate analysis for personalized banking, contractual obligations, state of health etc. Open banking can be defined as a collaborative model in which banking data is shared through APIs between two or more unaffiliated parties to deliver enhanced capabilities to the marketplace Concept 1: Each consumer owns the right to their data. Concept 2: This data is stored on their behalf securely and with their permission, in a financial institution like a bank. Concept 3: The consumer, has rights to share this data, via a secure channel that banks/financial institutions have to provide, to any other organisation of their choice, in order to get better services specially suited to them.
- Open banking is: The use of open APIs that enable third-party developers to build applications and services around the financial institution. Greater financial transparency options for account holders ranging from open data to private data. The use of open-source technology to achieve the above. It is linked to shifts in attitudes towards the issue of data ownership illustrated by regulations such as GDPR and concepts such as the Open Data movement. Relying on networks instead of centralization can facilitate the switching from using one bank's checking account service to another bank’s another app might help visually impaired customers better understand their finances through voice commands Before banks offered open banking, the closest thing available were aggregation sites They required requiring users to hand over their usernames and passwords for each account, then scraping the data off the screens of those accounts. This practice has security risks and the results of screen scraping are not always entirely accurate GDPR – The “right to be forgotten” significantly raises the stakes of data sharing. Another interesting twist revolves around the right to privacy. GDPR (General Data Protection Regulation), slated to take effect in the European Union in May 2018, imposes a substantial penalty for noncompliance— 4 percent of the offending institution’s global revenues (not profits). PSD2 – has not defined a precise technical standard. PSD2 went into full effect on 14 September, 2019, SEPA Payments – in order to solve cross border payments in EU, has as of 2019 34 members in EU SCA – Strong customer authentication - An important element of PSD2 is the requirement for strong customer authentication on the majority of electronic payments. The requirement ensures that electronic payments are performed with multi-factor authentication, to increase the security of electronic payments. Physical card transactions already commonly have what could be termed strong customer authentication in the EU (Chip and PIN), but this has not generally been true for Internet transactions. 3-D Secure has implementations by Mastercard (Mastercard Identity Check)[7] and Visa[8] which are marketed as enabling SCA compliance.
- Germany has been called “the world’s most open banking environment” EU - The most programmatic approach has been taken in the European Union, through both PSD2 and a broader effort to foster competition in retail banking through the United Kingdom’s Open Banking Standard. A key provision of PSD2 aims to foster competition and innovation for payments service provision in the European Economic Area by opening account access to nonbanks. The United Kingdom’s pending separation from the European Union is not expected to alter these data-sharing protocols, \ USA - By contrast, the absence of a centralized US approach to data governance has given rise to a series of fintech innovators as well as a patchwork of one-off bank agreements (such as partnerships struck in the United States by Chase and Wells Fargo with Xero and Finicity)—a model that is not scalable in a market with roughly 12,000 financial institutions. Recently, the US Office of the Comptroller of the Currency solicited public comments regarding potential issuance of a new special purpose charter enabling fintechs to engage in limited banking functions. While the charter’s intent focuses more on lending and cost of capital, it also represents a step toward making it easier for nonbanks to compete in financial services and conceivably paves the road for data-sharing protocols similar to PSD2. CHINA -
- Three major groups of providers in the financial system: Banks, Tech giants, startup fintechs. Startips are happy to control the FE and process, and leave the boring stuff to traditional banks. Others are just happy to offer services like overdraft prediction etc. New data sources and increased amount of data: transactional data, behavioral data, social data Main services: Deposits, savings, lending, payments Digitalization – transform data to meaningful information. Banks of the future will be increasingly run by technology, instead of trust. Mobile banking quickly became a table stake instead of nice-to-have. Digitalization is bringing about a change in the customer experience expected by certain customer groups in many industries. Today, large technology companies are setting the standards in terms of the speed of processes and decision-making, smooth interaction between provider and customer, intuitive user interfaces134 and individualization/personalisation135 of the services and offering Technology integration – AI, Cloud computing, etc. First mover advantage – How to get the users. Onboarding, different approaches. The problem of legacy systems. Disruption - First Principle Design VS Iterative Thinking Behavioral Science Integration - KYC – Know your costumer or Kill your costumer with paperwork. Ubiquity and Context-Aware Human/Machine Interactions Collaborative effort (Nordigen, Zuper) Creating new services creating - combining predictive analytics, artificial intelligence, and financing to enhance consumer and business offerings. PtP lending platforms Job loss - we need designers and programmers, not compliance officers and brokers. Example: Curriculums for economy students do not teach fintech… example: what is server?
- Consumer trust. This can be understood as trust in the security and handling of personal data ■ Technological BDAI platform. A flexible and modern system architecture permitting rapid response to new requirements (transformation capability), a suitable data architecture and tools for employing BDAI is meant here. It also means having the requisite employee skills and corresponding experience as well as the ability of the organization to work agilely throughout the company (see Chapter 4.2 ). ■ Customer reach. This refers to the number of customers that a provider can actually reach. Under the right circumstances, a high customer reach allows the provider to roll out operations very quickly to a large number of customers and therefore to potentially secure a first mover advantage. ■ Regulatory competence and industry know-how. This includes processes, methods and tools to be established as well as employees with the relevant knowledge of regulations and the industry. Compared with other sectors, regulatory competence is particularly relevant because the banking industry is a strongly regulated sector. Both processes and expertise are subject to the constant pressure to adapt to changing regulations. In addition, bank licences are a prerequisite for many Facebook Libra Amazon loans
- For example, global investment in fintechs employing BDAI grew by 62 percent between 2014 and 2016 to a total $2.3 billion
- ML is data analysis technology - Extracts knowledge without being explicitly programmed to do so! The idea is not new – Alan Tyring in 1950, assumed that really intelligent system as a whole would not be achieved by the detailed pre-programming of individual behavior patterns. Instead, machines would interact with their enviroments, learn, and thus become artificially intelligent Give a man a Fish and he will eat, learn him how to fish and he will not be hungry ever again! Supervised, - where observations contain labeled data Unsupervised- where labeled data is omitted reinforcement learning- where evaluation are given about how good or bad certain situation is like in games or driving vehicles Robo asvisors for investmants AI that executes trades - Around mid-2017, JPMorgan announced a first-of-its-kind AI to execute trades across its global equities algorithms business Bots for user support - The lawyer that is a bot At JPMorgan, a learning machine is parsing financial deals that once kept legal teams busy for thousands of hours. The program, called COIN, for Contract Intelligence, does the mind-numbing job of interpreting commercial-loan agreements that, until the project went live in mid-2016, consumed 360,000 hours of lawyers’ time annually Voice and chat driven virtual assistants - and services for them Capital one and amazon echo voice recognition Credit scoring - Help lenders get a more accurate picture of a consumer's financial situation and risk level. in China is now in the hands of a company called Alipay, which uses thousands of consumer data points — including what they purchase, what type of phone they use, what augmented reality games they play, and their friends on social media — to determine a credit score Hyper – personalization - can also look at consumers' transaction data to identify the best financial products
- Credit risk scoring and Automated loan decisions in the corporate and customer business. Monetization of customer data by banks. Money laundering prevention Risk analysis Automated loan decisions in the corporate customer business. This first illustrative use explores how BDAI applications can be used to extend the automation of credit decisions already in place in the retail customer business to cover simple corporate customers, too. In this way, BDAI could help to automate the process from credit application to credit decision in less complex cases in the corporate customer business. Optimization of compliance based on the example of money laundering investigations. This example highlights the potential that BDAI applications offer for core banking processes. Often, after a manual check, the automatically generated reference flagging a suspected case of money laundering proves not to be worthy of further investigation. It is conceivable that BDAI processes could help raise the quality of the hits, independently identify undetected patterns and significantly reduce the number of false alerts. Monetization of customer data by banks. This illustrative use case shows how transaction data can be used as a new source of income, thus opening up new business opportunities to banks in particular. Using BDAI applications, banks can evaluate the data available in-house in order to create personalised offerings, which permits banks to position themselves as an intermediary between customers and retailers.
- Why should treatment of your financial data be any different then treatment of your fitness data?
- For the vast majority of customers, automated credit decisions will drastically reduce the administrative burden and waiting times until a binding approval or denial of the loan is issued, improving the quality of the customer interface. In addition, better risk differentiation results in better identification of customers at risk of excessive debt, thereby protecting the bank against over-indebtedness by denying the loan. Transfer bank account and data Manage all from one place Deposits, Loans, Payments Personalized banking No feedback – education or coaching Response time Educating users - PFM as a google fit for your financial health Move to the edge and Integration with the assistant KYC financial crime prevention and AML (Anti-money Laundering) disaggregation of the value chain Pressure on banks More helpful tools Streamlined lending Automated accounting Fight fraud and waste New ways to pay (and accept payments) Innovative services Virtual assistants Automatic card bocking
- Do you know how to get your credit history? In Serbia 7-14 days or online via Internet explorer 8 with activeX controls enabled If someone wrote: “I promise I will pay you back so help me God” do not issue them loan ☹
- Example: TF 2.0 serialization of Tokenizer object is documented but does not work
- Sharing data: Any sharing you authorize puts your information into somebody else’s hands. Then you need to wonder how effective that third-party will be at protecting your information, and what they’ll do with the data. Open banking promises to let you make payments through social networks and let startups analyze your spending—but that might not be what you want. Traditionally, users trust banks 3 times more then online businesses when it comes to protecting personal information Legislature - Adoption of PSD2 and open banking. Opening legacy systems to APIs, adhere to local and global regulatory requirements like AML/CTF, Dodd Frank, FINRA, MiFID II, EMIR, FATCA, CRS, and IIROC requirements Skills shortage – Who are data scientists and what do they do Education – User says “You don’t know what is bast for me” and sets up week password and turns off 2 factor auth. Also behavioral anlyiss can seem like to invasive for users. Just because you can do something doesn’t mean it is ethical to do it. Conflicting goals - On the other hand, consumers are interested in preserving their privacy - The consumer is not simply the customer for BDAI products but indeed also an important supplier of data and it is not always entirely clear to the consumers whether and how their data is used. It is most likely that the asymmetries of power and information already discernible between consumers and companies will continue to grow. Consumers have difficulties foreseeing the consequences of sharing their data
- Congratulations, @zuperbank (Grind category)