Redefining Banking with Natural Language Fintech

Fintech for Banking

Banking has always been one of the slowest business sectors when it comes to adopting new technology. Fintech has brought some speed and produced exciting use cases of innovation in that field. One of the most exciting ones is the use of Natural Language Analytics in areas like personal finance management, risk assessment and financial assets management. Take a look!

Personal Finance Management – “How much am I spending on fuel?”

Most of us are still managing our personal finance by going through our bank transactions with a calculator in one hand and some creased receipts in the other. Even if we have an app or use spreadsheets it isn’t very effective, because we still have to enter all the the transactions one by one.

If our bank app and online banking already have the data we need, wouldn’t be cool if they could simply provide a better way for us to manage our personal finance? Some european banks are currently working on new approaches with Natural Language Analytics.

The goal is to allow users to analyse the data through a search box. Just need to type questions in plain language like “How much did I spent on restaurants last month?” or “What’s the average amount spent on fuel per month?”.

Wizdee is currently working to make this possible, let me explain you how. Wizdee classifies every bank transaction with a tag, so users don’t need to lose time doing it. It can be restaurants, groceries, fuel, mortgage and so on.

Users enter their query and in sub seconds Wizdee retrieves an answer with the best visualisation, be it a chart, a table or just a number.

If typing seems too boring, users can speak the queries and get answers on the spot.


Risk assessment – “What is the average risk for last six months of the retail stocks?”

An international bank reached Wizdee for a way of their risk managers to easily evaluate propositions and products, increasing their productivity.

Risk analysis models can be really complex. Querying data using everyday language it’s a much simple and faster way to assess risk.

Risk managers can easily do queries like ‘What is the risk of Fund X?’ or ‘Average risk for last six months of the retail stocks’.


Financial Assets management – “Show me the closing value of the portfolio”

Natural Language Analytics can also improve processes to manage, value and account portfolios assets.

Users can do queries like ‘Evolution of the price of a security over the year’ or ‘Closing values of a portfolio’ or even ‘Taxes associated with asset Y’.

It’s much easier to just enter queries and automatically get a chart than going through all the data and create charts yourself.


It’s interesting to watch a change in the banking sector attitude towards technology. They are starting to see it as an opportunity instead of a threat, identifying ways to take advantage from it.

Using Natural Language query for BI and Analytics is just one example. It’s helping banking leverage their data to provide financial services efficiently and quickly. Can’t wait to see what comes next!


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