fincommend - revolutionize retail investing

Elevator Pitch

fincommend = financial recommendations

Retail investors currently do not allocate appropriate time and attention to their own investments. Unfortunately, investing is not associated with fun! We want to change this! fincommend is the Spotify for capital market recommendations. We give retail investors fincommend for new amazing opportunities: personalized portfolio analysis, social portfolio analysis, a time budget to manage the portfolio (i.e. 10 minutes/day) and a new coaching approach - the game changer for retail investing.

Type of project

fincommend mobile is offered as a mobile app to bank clients for core analysis results according to time budget.
fincommend online is a web app for parameterization of rules, detailed performance analysis and coaching communication.

MVP

MVP1: Bank client user to define his time budget for portfolio management
MVP2: Bank client user to use social portfolio analysis and recommendations
MVP3: Bank client user to define his personal target portfolio preferences
MVP4: This is our core - result screen where fincommends are displayed according to selected time budget, social portfolio analysis and time budget; (taxonomy: fincommend = name is the name of our app as well as single financial recommendation we create on the basis of the defined rules; as core result we display to our bank client user a fix number of fincommends fitting to his personal time budget!!)

Target group

fincommend is a product for banks who start bringing new service philosophy and technology to existing retail investment clients
offering fincommend will create a win-win situation for clients and banks. Clients get free capital market education by defining fincommends, professionalize their own portfolio management skills within the given time budget, increase portfolio performance and start having fun with investments. Banks will benefit from increased order volume and the client coaching channel to place products
fincommend will enable traditional banks to start displaying fintech competence to their innovation-affine clients and position themselves in the fintech product market

Use Cases

We define two types of users:

The bank client user accesses the system to receive fincommends according to his personal preferences and time budget.
The coach user who trains the user in creating rules and explains to the bank client new products and new markets information.

The following use cases are implemented:

Bank client user to define his time budget (i.e. five/ten minutes per day or ten/twenty minutes per week) and amount of fincommends received.
Bank client user to activate the "social button", then the bank will add the user to the portfolio social users, who run social similarity algorithms across the social portfolios, privacy aware.
Bank client user to define target portfolio: i.e. certificates/ETF's on exchange indices/industry sector indices/single shares for focused fincommends
Coach user to send messages to user regarding new products relevant for the bank client user
Bank client user to monitor all fincommends in combination with trades: History helps to explain own P&L as well as the validity of rules.
Bank client user receives list according to his own time preferences (i.e. 5 fincommends for five minutes time at a given point in time per day)
Bank client user - within the list - to rate the fincommends as useful and not useful which will be recorded by the application and used to filter future fincommends
Bank client user receives his performance KPI's, risk KPI's and his own KPI for time efficiency

Challenges & solutions

Getting client data access which we resolved thanks to the API by Figo
Getting access to market data which we resolved thanks to the API by Finanzen 100
Implementation of analysis methodology which is really unique and fun to use in financial markets environments: We used state-of-the-art data mining algorithms, e.g. collaborative filtering and applied similarity measures in analogy to Spotify. We use the user historical rating information to finally produce the fincommend list. We resolved all arising complexity issues by scope reduction - focussing on shares as product for the prototype.
Telling a good story for financial markets is always a challenge! We tried to not use the expert language in our documentations and produced for our own discussions the marketing beacon of "the retail investor having fun investing using Spotify-like functions”.

Scalability

Build a "simulation version" where client does not need to upload his real portfolio (like we do with the Figo API currently) but can generate a "virtual" portfolio to start learning
Refine app for products: bonds, ETF's, certificates should be included in analysis
Refine methodology: More alternative analysis approaches to be used to produce fincommends
Clearly elaborate the role of the capital markets retail investment coach in the bank. I.e. create a new capital markets coaching concept containing "news", "sparring" and "review" client coaching sessions.
Check time budget function for gamification options
Check coaching function for new hardware components like "coaching table"

Team

-Christian Burkhardt (design, front-end), @cinovo.de -Michael Wittig (concept, back-end, infrastructure), @hellomichibye -Ben Lebherz (concept, back-end, cloud), @ben_leb -Mark Kibanov (concept, data science, algorithms), @betrium -Oliver Schoch (design, documentation, presentation), @schofox