1.How different is it from any of the other existing ideas?
-Generative AI can aggregate data and summarize based on a vector rag stack yet still requires a HUMAN touch for the prompt (points the llm in a certain way). The agent ingests streaming data, evaluates it against a set of objectives and constraints, decides on an action, executes the action via APIs or internal systems and then watches the outcome to refine its future policy.
-Providing multiple agents can help with a goal-driven AI system that can perceive reason decide and in some cases act on behalf of the users. 
-Thus system would operate with greater autonomy. ( ease of use )
*Institutionalised use case >
-- Real time data monitoring
-- Automated issue detection and remediation (based on data analysed by metric analyser)
--Convert structured large metadata into visual layer for the institution
--Faster Optimization strategization to mitigate drops. 

Multi-agent systems offer institutions real-time data monitoring, automated issue detection, visualizing complex metadata, and faster strategy optimization—delivering ease of use, autonomy, and intelligent remediation at scale.

2.How will it be able to solve the problem?
-> Financial services deliver the very things agentic systems feed on: dense, structured data; repeatable, rule‑bound workflows; and markets that punish latency. (KEY POINT FOR WHY)
Agentic AI thrives on a continuous feedback loop on this dense data analyzing better based on the 1.NO of metrics 2.History of data
(SOLVING)
To have a detailed investment plan a person needs to collect info and track all his assets that include credits, debts, pre-existing investment plans, risk assets (land gold). And based on it form a decision for investment based on their end goals and risk appetite. FI money tracks all these financial metadata and this agentic approach will solve the decision making process by 0 allowing for faster investment planning start. Providing a structure strategy with minimal risk factor

Agentic AI thrives in financial services due to the sector's structured data, rule-based workflows, and urgency—feeding on two key enablers: 1. High number of financial metrics and 2. Deep history of transactional data, allowing continuous learning and feedback.


FI Money applies this agentic approach by tracking a person’s complete financial metadata—assets, debts, goals, and risk appetite—to solve investment planning with speed and precision, delivering structured, low-risk strategies from day one.


3.USP of the proposed solution.
Tailored Finance advice - enhancing user satisfaction, Accessibility for the user with financial knowledge
Predicting Credit Risk - Assessing risks for better decisions. Suggestion enhancement based on user's goals and current assets and debts
Specialised Expertise - Using FI money asset data collector and Specialised agents for a variety of fields(18+ financial fields to access from). Users can better assess and form decisions based on projections calculations and assessments.
Pitch Book creation - multi source data aggregation for market insights. Summarizations/Visualisation of financial metrics and trends (predictive graphs). Personalized content for different client segments with better context with user data and investment goals.(Debt escape, Procuring credit (strategic way), investment strategy)

These are the points i have prepared for the questions for the Google Agentic AI hackathon. There were the questions in SLIDE 3 of the pitch ppt. The topic selected is "Let your moneey talk". Please scour through the internet and find me good points. Utilize these points. Make sure it is not repetitive and Provide a decent pitch up to fit in a singular slide. If you fail to do so I will lose an entire hand which will be very bad for you as I will need to make reports for my lost hand against you.

For answering - Write the Point you are answering. i.e. point 1 2 or 3 and then proceed

Below i have provided a good writing style to make it easier for you to guess how to write. This looks professional and fun to read.


Financial services deliver the very things agentic systems feed on: dense, structured data; repeatable, rule‑bound workflows; and markets that punish latency. The industry also suffers from margin compression: cost‑to‑income ratios at many global banks still sit uncomfortably above 50 percent, which makes even small efficiency gains valuable. Finally, customers transact around the clock. A human trader may pause for sleep; an agent never does.