Predictive Analytics and Artificial Intelligence in Financial Services

Predictive Analytics: the use of data to predict future trends and events. It uses historical data to forecast potential scenarios that can help drive strategic decisions (What is Predictive Analytics? – Harvard Busines School)

Generative AI: any AI system whose primary function is to generate content; This is in contrast to AI systems that perform other functions, such as classifying data (e.g., assigning labels to images), grouping data (e.g., identifying customer segments with similar purchasing behavior), or choosing actions (e.g., steering an autonomous vehicle). (CSET)

Machine Learning (ML): a field of computer science that uses algorithms to process large amounts of data and learn from it. Unlike traditional rules-based programming, ML models learn from input data to make predictions or identify meaningful patterns without being explicitly programmed to do so (AI in the Securities Industry, Financial Industry Regulatory Authority)

Of Cyborgs and Centaurs
Of Cyborgs and Centaurs

Deep Learning: a sub-field of machine learning that focuses on the development and application of algorithms inspired by the structure and functioning of the human brain, specifically artificial neural networks. It involves training neural networks with a large amount of labeled data to recognize complex patterns and make intelligent decisions or predictions.

Neural Networks: a set of interconnected processing units or nodes, designed to simulate the behavior of neurons in the human brain. Each node receives input data, processes it, and produces an output that is passed to the next layer of nodes. These networks can have multiple layers, enabling them to learn and extract high-level representations of data. By adjusting the weights and biases of the connections between nodes during training, neural networks can adapt and improve their performance.

Large Language Model (LLM): a type of AI system that works with language. In the same way that an aeronautical engineer might use software to model an airplane wing, a researcher creating an LLM aims to model language, i.e., to create a simplified—but useful—digital representation. The “large” part of the term describes the trend towards training language models with more parameters.1 A key finding of the past several years of language model research has been that using more data and computational power to train models with more parameters consistently results in better performance. (What Are Generative AI, Large Language Models, and Foundation Models? – Georgetown University Center for Security and Emerging Technology)

Financial Services Standards of Conduct

Earlier this year the Securities and Exchange Commission (SEC) proposed rules intended to protect investors from conflicts of interest arising from the use of predictive analytics and artificial intelligence by Broker/Dealers (B/D) and Registered Investment Advisers (RIA). The intent here is to prevent model bias where firms may put their interests before clients interests, put simply advisers and broker-dealers could use predictive analytics and AI to increase commissions. See Fact Sheet.

B/Ds and associated persons are held to a “best interest” standard of conduct. SEC Regulation Best Interest requires brokers to “Act in the best interest of the retail customer at the time the recommendation is made, without placing the financial or other interest of the broker-dealer ahead of the interests of the retail customer” (SEC Reg BI Final Rule).

Under federal law as a State of New Jersey RIA, Coroebus Wealth Management is a fiduciary. Consider fiduciary duty a standard of conduct comprising of a duty of care and a duty of loyalty (SEC Interpretation of the Investment Advisers Act of 1940). We do not act as a broker in transactions or earn commissions. We manage portfolios and charge a fee based upon the dollar amount of assets under management (AUM). B/Ds are incidentally excluded from the Advisers Act.

Suggested reading of cyborgs and centaurs: Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality – Harvard Business School

Coming Soon; Pythia is predictive analytics for entrepreneurs…

First Time Homebuyer Resources

Happy Labor Day to all!

Despite our momentum the past month was challenging. Our service mark for Pythia was in danger of being cancelled until just a few days ago. I hadn’t realized how important this project has become and what it might mean to our longevity until we were at risk of losing it.

Six years later… we will continue to chip, or chop away at this project until we have a minimum viable product to release. We do not yet have a release date, but if you would like to join our group of 100 early adopters you can sign up here.

We will enjoy the long weekend but I would like to share a not yet exhaustive list of resources for first time homebuyers. We will continue to update this post with more resources moving forward.

U.S. Department of Housing and Urban Development (HUD) – Federal Housing Administration (FHA)

New Jersey Housing and Mortgage Finance Agency (NJHMFA)

NYC Housing Preservation and Development HomeFirst Down Payment Assistance Program

Fannie Mae HomeReady Mortgage

Freddie Mac HomePossible Mortgage

U.S. Department of Agriculture Single Family Housing Programs

U.S. Department of Veterans Affairs (VA) Housing Assistance

My Home by Freddie Mac

HUD Good Neighbor Next Door (GNND) Program

National Homebuyers Fund, Inc. (NHF)

Neighborhood Assistance Corporation of America (NACA)

Consumer Financial Protection Bureau (CFPB)- Key Terms

In addition to the above resources many banks and lenders have similar programs to assist FTHB in making the leap into ownership.

Having and executing a financial plan may be the difference between knowing what kind of home to target and simply hoping for the best. Use our worksheets or contact me directly to start a conversation.

Happy J’ouvert!