ai and deep learning in finance applications limits
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AI and Deep Learning in Finance: applications, limits, impact and - PowerPoint PPT Presentation

Sep 20, 2022 •180 likes •349 views

AI and Deep Learning in Finance: applications, limits, impact and use-cases Company Axyon AI leverages the most recent advancements in AI and deep learning to create business applications for capital markets and asset management Main investors

  1. AI and Deep Learning in Finance: applications, limits, impact and use-cases

  2. Company Axyon AI leverages the most recent advancements in AI and deep learning to create business applications for capital markets and asset management Main investors Main partners

  3. AI in Finance

  4. Banking and technology Corporate and investment Retail banking banking Serves individuals and entities that are not companies Serves corporates and large organizations (e.g. governments) Cutting-edge technology AI/Deep Learning AI/Deep learning-powered products

  5. Applications

  6. Credit Decisions Digital banks use AI-algorithms to use alternative data to evaluate loan eligibility . What Automobile lending companies in the U.S. reported success with AI. Bringing AI on board cut losses by 23% annually . Artificial Intelligence provides a faster, more accurate assessment of a potential borrower, How at less cost, and accounts for a wider variety of factors, which leads to a better-informed, data-backed decision

  7. Personalized Banking Smart chatbots provide clients with comprehensive self-help solutions while What reducing the call-centers’ workload, and they get smarter every day . AI-based intelligent systems track income, essential recurring expenses, and spending How habits and come up with an optimized plan and financial tips.

  8. Trading Trading systems provide recommendations for trading and asset management, What identifying the assets and suggest investment strategies . AI/Deep Learning Trading Systems monitor both structured (databases, spreadsheets, How etc.) and unstructured (social media, news, etc.) data in a fraction of the time it would take for people to process it

  9. Limits

  10. Banks Needs Always up and running ● ● Low risk Compliant with regulations ● Characteristics Slow and bureaucratic ● ● High transparency High impact with small improvements ●

  11. Startups Needs Clarity on the whole process ● ● Internal sponsor Clear view of: viability feasibility desirability ● Characteristics Fast ● ● Make mistakes Highly innovative ●

  12. Why AI AI/Machine learning Traditional algorithms Big Data Small datasets DATA Alternative data sources, unstructured data Old data sources, structured data (news, social media) only Learn how to solve problems by themselves, Do not learn without having to be specifically programmed Based on human intuitions MODELS Free-form approach, adaptive Parametric approach New, complex, non-linear Simple, Linear PATTERNS Containing predictive value (Alpha) With little predictive value Data-driven Driven by intuitions DECISIONS Unaffected by cognitive/behavioral bias Prone to cognitive bias/behavioral bias

  13. Axyon: asset management (2) Fund’s systems Context market data Predicted target APIs metrics (rankings by Economics/Fundamentals return, volatility, Sharpe; Axyon IRIS correlation) SFTP AI engine Sentiment data Several supported E-mail prediction horizons Fund’s proprietary data Improved strategies Target assets market data and positions

  14. Axyon: Loan syndication Smart and predictive Refinitiv analytics and market SynFinance helps to LPC + insights improve bank’s position in Proprietary the syndicated loan market Used by originators and data syndicators from origination phase to execution Liquidity analysis Market analysis Lead generation

  15. Contacts Axyon AI SRL Modena, Italy London, United Kingdom info@axyon.ai Giacomo Barigazzi giacomo.barigazzi@axyon.ai

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