AI-Powered Financial Analysis and Recommendation System

Processing various content feeds in real-time and boosting the productivity of financial analysts and client relationship manager

Technologies / Platforms
The system uses machine learning techniques to process various content feeds in real-time and boost productivity of a financial analyst or a client relationship manager in such domains as wealth management, commercial banking, fund distribution.

Tech Stack / Platforms


  • Investment portfolio analysis and optimization
  • Fund recommendation based on quantitative analysis and backtesting
  • Client prioritization – determines which clients to call first and why, based on clients’ portfolios, transactions, CRM notes, and analyses of market events
  • Content recommendations – recommends news and research papers relevant to a client’s business and current market situation
  • Chatbot with natural language interface – possesses ability to answer advanced questions about specific funds, stocks, or market events
  • Real-time analysis of multiple data feeds from Thomson Reuters, Morningstar, and other sources

How it Started

June 2015

The client contacted Itexus via a referral from a previous client with an idea for a FinTech product. Initially, the functionality of the system was very small: it was meant to provide quantitative recommendations of liquid alternatives (mainly Hedge Funds) for client portfolios by analyzing mutual correlations, risk/return ratios, etc.

Discovery Phase

July 2015

We started the project with a discovery phase, during which the Itexus team translated the client’s idea into a requirements specification. Our Software Architect analyzed suggested integrations and came up with an estimate and architecture for the MVP of the system.

First Version

October 2015

The initial MVP allowed a user to upload their portfolio, show quantitative analysis of the portfolio, and receive a recommendation for its optimization using pre-configured securities. It had two integrations:

  • Xignite — to obtain daily price changes for a variety of securities from NYSE and NASDAQ;
  • Raise Partner – a quantitative analysis engine that calculated various financial metrics (e.g., Risk, Return, Sharpe Ratio, Skewness, Max Drawdown, Diversification, and Volatility) of the portfolio, including the portfolio as a whole as well as the contributions of each instrument in the portfolio. This also allowed portfolio optimization to achieve various goals (e.g., minimize risk, maximize return).


IBM Watson

November 2015


During the implementation of the MVP, the client significantly changed the idea for the product. He saw a tremendous opportunity in combining quantitative financial analysis with AI and NLP (natural language processing). We created a quick PoC that combined IBM Watson conversation services with the already-implemented portfolio analysis and recommendation service. This would allow potential users to ask questions about their portfolios and current market conditions in natural language and receive thorough, personalized analyses using various data sources and quantitative engines.



January 2016


The PoC had a tremendous success. It allowed the client to enter two accelerator programs – Fintech Sandbox in New York and, later, TechStars London. The startup raised a seed round investment from TechStars, as well as an investment from Thomson Reuters at the end of the program. The client moved from New York to London for three months to participate in the TechStars accelerator program. During that time, Itexus’ management and technical team visited the client three times for one-week stints and participated in the brainstorms and product strategy sessions. Bonding events led to the development of a  close partner relationship between Itexus and the client, which resulted in Itexus becoming an investor and a technological partner of the product.



May 2016


While the Client was taking care of the business side of the startup at TechStars, our development team has been working on expanding the conversation interface with new intents and new data providers. We integrated various APIs from Morningstar including Morningstar data feed on funds, Morningstar reports, and Morningstar API center. This resulted in an increase of the number of various questions about different funds the system could handle. The updated functionality also supported analysis of market events and their impact on user’s portfolio. The system explained which factors (interest rates, oil prices, etc.) affected the performance and volatility of the funds or a portfolio.


Thomson Reuters

June 2016


After graduating from TechStars program, the client signed an agreement with Thomson Reuters, which then became a strategic partner/reseller of the product, as well as an investor. Thomson Reuters was interested in adding AI FinTech services to its platform and offering these features to its clients. This collaboration led to the integration of multiple Thomson Reuters services:

  • Machine readable news
  • IntelligentTagger (OpenCalais) NLP processing service
  • Real-time and historical stock quotes
  • Stock and Fund analytics
  • Bear/Bull forecasting services

All this information was integrated with the existing conversational interface and available to users via questions and answers.


Third-Party Integrations

AI-powered financial analysis and recommendation system – third-party integrations

  • Xignite provides cloud-based financial market data distribution solutions for fintech companies and financial services providers. It is integrated to obtain daily price changes for a variety of securities from NYSE and NASDAQ.
  • Raise Partner is a quantitative analysis engine that calculates various portfolio financial metrics and allows portfolio optimization to achieve various goals.
  • IBM Watson Conversational Service enables conversational interfaces to be integrated into any application, device, or channel. It has been integrated as a complement to the portfolio analysis and recommendation service features, to allow potential users to ask questions in natural language and receive a thorough, personalized analysis based on multiple data sources and quantities engine.
  • Morningstar is a financial services company that provides a range of investment research and investment management services. It’s integrated to provide real-time global data on various types of securities.
  • Thomson Reuters provides expert resources, tools, and technology in the areas of tax, law, and risk management, helping financial services companies deliver world-class services to their clients. We’ve integrated various Thomson Reuters services into the existing conversational interface and made the information accessible to users through questions and answers.

Change of the Strategy

January 2016 – Summer 2017

The client’s vision for an AI-based system able to answer any financial question and provide users with personalized content ended up being too big to implement in such a short time frame and with the seed round investment budget. The product was also arguably too big to sell, as it envisioned a significant reimagining of the way financial institutions work. Meanwhile, seed round money began to run out over time. Eventually, development was put on pause as the Client took time to rethink their strategy.

AI Content Recommendation and Client Prioritization System

August 2017

After a few months’ pause, the Client came back to us with a new product strategy. He decided to narrow the focus in the near term, instead aiming to start build incrementally with smaller, more practical steps. This meant starting with intelligent APIs that could supplement existing processes and systems used in the financial world, rather than aiming to replace them. This also meant that each API could be sold separately. The first version of this new product contained two APIs:

  • Content personalization
  • Client prioritization

With the help of various machine learning algorithms, the APIs analyze all the information financial institutions have on their clients: CRM data and notes, recent transactions, portfolios, market information in the news about their sector/location, etc. This solution could also be used to improve the efficiency of client relationship managers. The system recommends which clients should be called first, as it indicates if there may be a problem that needs to be solved or if there is an opportunity for cross-selling. Managers could also utilize the system to be better prepared for meetings by reading relevant content (e.g., news, bank’s analytical material, about products, industry, and trends) about a given client through the platform . The content personalization API analyzes real-time data feeds and processes them with various NLP techniques. With the help of machine learning algorithms, it recommends which content is most relevant to a given client, thus saving time spent on manual preparation while still providing a service that is much more personalized to individual end clients.



First Real Clients

April 2018 – May 2018

A large European commercial bank and a U.S. asset management company became the first clients of the AI-powered platform. In May 2018, the client successfully secured Series A investment funding of $5 000 000 from a strategic investor.

Present Day

The project is ongoing to this day, with a focus on new client acquisition and the addition of increasingly more  AI and financial functionality to the product.

Technical Solution

Machine Learning

  • NLTK
  • TensorFlow
  • Spycy
  • Scikit-learn
  • Neo4j
  • IBM Watson


  • Entity extraction
  • Text classification
  • Topic modelling
  • Sentiment analysis
  • AI search

Recommendation system

  • Hybrid Collaborative Filtering
  • Similarity Analysis
  • Deep Neural Networks

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United States

8, The Green, STE road, Dover, DE 19901


Żurawia 6/12/lok 766, 00-503 Warszawa, Poland

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