Key Points

1. AI fits many use cases in finance today
2. analytics, sales & cash flow forecasting, underwriting, credit decisions, product risk, AML, fraud, cyber risks, market segment trends, policy recommendations ( SLI v OKRs)

References

Key Concepts

Key Standards and Communities are the foundation for Digital Finance

There are many DeFi platforms today to review. My focus has been on open communities engineering solutions built on open standards, trust, compliance and governance first. I'm continuing to track the progress on trust models, digital assets, transactions, settlements in finance ( RLN, RSN, ISO-20022 ), supply chain ( GS1 ), digital identity ( ISO 5009 ) and privacy ( ISO-27001, 27018, 27019 ). While a lot of the existing architectures need to evolve, these directions offer promising results.

Digital Finance

Citi Institutional Interoperability Report.   link 

Citi Institutional Interoperability Report. url

Generative AI: Unlocking its potential in finance - webinar

1. Gen AI creates new solutions, results based on the LLM models and the custom problem
2. works when there are common patterns in the data 
3. Internet content crawlers pull in specific data to specific LLM models for training / calibration
4. effective when using broader data sets vs specific enterprise data sets for predictions
   1. PTP with Genesys IVR did custom CustomerCare voice recognition
5. output = text, images, code 
6. Gen AI processes whole sentences and can understand context from trained models with added inputs
   1. Transformers were able to process sentences effective 2017 
7. Gen AI in healthcare can improve the quality of data collected and suggest possible strategies for a patient ( Nuance )
8. Microsoft works with Tier 1 Bank solutions focusing first on apps that help their staff ( smart assistants LIKE DMX STM - Smart Trade Mgr for auto pricing )
9. Fin firms will buy and build custom solutions to their software
10. AI projects often meet delivery dates
11. AI app areas:  personal productivity on IT staff on their tasks ( before direct customer interfaces ), sales & customer service on moderately complex products ( mortgages, loans, trade finance value chain ( or some components / steps ), insurance, wealth ), software development tools ( quality and productivity )
12. Governance strategies for AI 
    1. Responsible use of AI 
    2. Strategy add governance on top of existing governance for a solution
    3.  
13. How AI is regulated now
    1. EU AI act focus - current version ? on Gen AI - transparency on data,models,rules, registration of AI solutions, IP rules 
    2.  
14. M365 copilot EAP - personal assistant in MSFT products
    1. can summarize an email details 
    2. ask to create a response given concerns in the email etc
    3. start a Word doc given some other content added
    4. ask it to coach / improve the style of response
15. Working Swift on M365 EAP 
16. Customer survey – 70% more productive, better work quality, saved 1/3 of time
17. Advice on Gen AI rollouts
    1. ai tech team skills
    2. reuse strategies for AI component services
    3. focus on specific business use cases for teams
    4. staff first use cases before customer use cases
    5. internal productivity - fast time to value - ttv, low risk, 3B sourcing, 
    6. rethink business models, processes in the VCE for automation, AI, DLT, trust impacts
18. Tips
    1. when to use Gen AI vs custom AI 
    2. rarely need to create custom Gen AI LLM model vs reuse and inject new data, parms
19. Impact on Financial services of Gen AI - better value product w bigger audience, more personalization, compliance growth

BCS - Better Compliance Services - business partnerships - custom solutions w common services like OTI

Quantum Computing Updates

https://www.eetimes.com/document.asp?doc_id=1335027

Potential Value Opportunities

Quantitative Analysis Definitions - investopedia

Understanding Quantitative Analysis

Quantitative analysis (QA) in finance refers to the use of mathematical and statistical techniques to analyze financial & economic data and make trading, investing, and risk management decisions.

QA starts with data collection, where quants gather a vast amount of financial data that might affect the market. This data can include anything from stock prices and company earnings to economic indicators like inflation or unemployment rates. They then use various mathematical models and statistical techniques to analyze this data, looking for trends, patterns, and potential investment opportunities. The outcome of this analysis can help investors decide where to allocate their resources to maximize returns or minimize risks.

Some key aspects of quantitative analysis in finance include:1

• Statistical analysis - this aspect of quantitative analysis involves examining data to identify trends and relationships, build predictive models, and make forecasts. Techniques used can include regression analysis, which helps in understanding relationships between variables; time series analysis, which looks at data points collected or recorded at a specific time; and Monte Carlo simulations, a mathematical technique that allows you to account for uncertainty in your analyses and forecasts. Through statistical analysis, quants can uncover insights that may not be immediately apparent, helping investors and financial analysts make more informed decisions.
• Algorithmic trading - this entails using computer algorithms to automate the trading process. Algorithms can be programmed to carry out trades based on a variety of factors such as timing, price movements, liquidity changes, and other market signals. High-frequency trading (HFT), a type of algorithmic trading, involves making a large number of trades within fractions of a second to capitalize on small price movements. This automated approach to trading can lead to more efficient and often profitable trading strategies.

• Risk modeling - risk is an inherent part of financial markets. Risk modeling involves creating mathematical models to measure and quantify various risk exposures within a portfolio. Methods used in risk modeling include Value-at-Risk (VaR) models, scenario analysis, and stress testing.3 These tools help in understanding the potential downside and uncertainties associated with different investment scenarios, aiding in better risk management and mitigation strategies.
• Derivatives pricing - derivatives are financial contracts whose value is derived from other underlying assets like stocks or bonds. Derivatives pricing involves creating mathematical models to evaluate these contracts and determine their fair prices and risk profiles. A well-known model used in this domain is the Black-Scholes model, which helps in pricing options contracts.4 Accurate derivatives pricing is crucial for investors and traders to make sound financial decisions regarding buying, selling, or hedging with derivatives.

• Portfolio optimization - This is about constructing a portfolio in such a way that it yields the highest possible expected return for a given level of risk. Techniques like Modern Portfolio Theory (MPT) are employed to find the optimal allocation of assets within a portfolio.5 By analyzing various asset classes and their expected returns, risks, and correlations, quants can suggest the best mix of investments to achieve specific financial goals while minimizing risk.

The overall goal is to use data, math, statistics, and software to make more informed financial decisions, automate processes, and ultimately generate greater risk-adjusted returns.

Quantitative Analysis vs. Qualitative Analysis

Quantitative analysis relies heavily on numerical data and mathematical models to make decisions regarding investments and financial strategies. It focuses on the measurable, objective data that can be gathered about a company or a financial instrument.

But analysts also evaluate information that is not easily quantifiable or reduced to numeric values to get a better picture of a company's performance. This important qualitative data can include reputation, regulatory insights, or employee morale. Qualitative analysis thus focuses more on understanding the underlying qualities of a company or a financial instrument, which may not be immediately quantifiable.

Quantitative isn't the opposite of qualitative analysis. They're different and often complementary philosophies. They each provide useful information for informed decisions. When used together. better decisions can be made than using either one in isolation.

Some common uses of qualitative analysis include:6

• Management Evaluation: Qualitative analysis is often better at evaluating a company's management team, their experience, and their ability to lead the company toward growth. While quantifiable metrics are useful, they often cannot capture the full picture of management's ability and potential. For example, the leadership skills, vision, and corporate culture instilled by management are intangible factors that can significantly impact a company's success, yet are difficult to measure with numbers alone.
• Industry Analysis: It also includes an analysis of the industry in which the company operates, the competition, and market conditions. For instance, it can explore how changes in technology or societal behaviors could impact the industry. Qualitative approaches can also better identify barriers to entry or exit, which can affect the level of competition and profitability within the industry.
• Brand Value and Company Reputation: The reputation of a company, its brand value, and customer loyalty are also significant factors considered in qualitative analysis. Understanding how consumers perceive the brand, their level of trust, and satisfaction can provide insights into customer loyalty and the potential for sustained revenue. This can be done through focus groups, surveys, or interviews.

• Regulatory Environment: The regulatory environment, potential legal issues, and other external factors that could impact a company are also analyzed qualitatively. Evaluating a company's compliance with relevant laws, regulations, and industry standards to ascertain its legal standing and the potential risk of legal issues. In addition, understanding a company's ethical practices and social responsibility initiatives, that can influence its relationship with stakeholders and the community at large.

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