Building Personal Wealth Advisers with Bank APIs

In the previous chapter, we analyzed the behavior of a sell-side of the exchange. We also learned about sentiment analysis and gained in-depth knowledge of the subject by learning how to analyze market needs using sentiment analysis. We then learned a bit about Neo4j, which is a NoSQL database technique. We then used Neo4j to build and store a network of entities involved in security trading.

In this chapter, we will focus on consumer banking and understand the needs of managing customer's digital data. Then, we will learn how to access the Open Bank Project, an open source platform for open banking. After that, we'll look at an example of wrapping AI models around bank APIs. Finally, we will learn about document layout analysis.

We will cover the following topics in this chapter:

• Managing customer's digital data
• The Open...

Managing customer's digital data

In this era of digitization, there is no reason that money cannot be 100% transparent or that money transfers can't happen in real time, 24/7. Consumers have the right to their data as it represents their identity. Whether it is possible to or not, we should be consolidating our own data – realistically, this should be happening today and in the coming few years. It is best to consolidate our banking data in one place; for example, our frequent flyer mileage. The key point is that there shall be two tiers of data architecture – one for consolidation (including storage) and another for running the artificial intelligence services that will be used to analyze the data through the use of a smart device, also known as mobile applications. It can be painful to design an AI algorithm without understanding what is going on at the data consolidation layer.

Here, our data source could be identity data, bio/psychometric data...

The Open Bank Project

The world's most advanced policy that allows consumers to consolidate their own data is called the Open Banking Project. It started in the UK in 2016, following the European's Directive PSD2 – the revised Payment Services Directive (https://www.ecb.europa.eu/paym/intro/mip-online/2018/html/1803_revisedpsd.en.html). This changed the competitive landscape of banks by lowering the entry barrier in terms of making use of banks' information for financial advisory reasons. This makes robo-advisors a feasible business as the financial data that banks contain is no longer segregated.

The challenge with this project is that the existing incumbent dominant banks have little incentive to open up their data. On the consumer side, the slowness in data consolidation impacts the economic values of this inter-connected network of financial data on banking services. This obeys Metcalfe's Law, which states that the value...

Performing document layout analysis

In ML, there is a discipline called document layout analysis. It is indeed about studying how humans understand documents. It includes computer vision, natural language processing, and knowledge graphs. The end game is to deliver an ontology that can allow any document to be navigated, similar to how word processors can, but in an automated manner. In a word processor, we have to define certain words that are found in headers, as well as within different levels of the hierarchy – for example, heading level 1, heading level 2, body text, paragraph, and so on. What's not defined manually by humans is sentences, vocabulary, words, characters, pixels, and so on. However, when we handle the images taken by a camera or scanner, the lowest level of data is a pixel.

Steps for document layout analysis

In this section, we will learn how to perform document layout analysis. The steps are as follows:

1. Forming...

Cash flow projection using the Open Bank API

In the future, we will need robo-advisors to be able to understand our needs. The most basic step is to be able to pull our financial data from across banks. Here, we will assume that we are customers of consumer banking services from the US who are staying in the UK. We are looking for wealth planning for a family of four—a married couple and two kids. What we want is a robo-advisor to perform all our financial activities for us.

We will retrieve all the necessary transaction data from the Open Bank Project (OBP) API to forecast our expenditure forecasting via Open Bank API. The data that we will be using will be simulated data that follows the format specified in the OBP. We are not going to dive deep into any of the software technologies while focusing on building the wealth planning engine. The family description we'll be using has been obtained from the Federal Reserve (https://www.federalreserve...

Using invoice entity recognition to track daily expenses

While we are always dreaming for the end game of digitization through AI in finance, the reality is that there is data that's trapped. And very often, these expenses come in the form of paper, not API feeds. Dealing with paper would be inevitable if we were to transform ourselves into a fully digital world where all our information is stored in JSON files or SQL databases. We cannot avoid handling existing paper-based information. Using an example of a paper-based document dataset, we are going to demonstrate how to build up the engine for the invoice entity extraction model.

In this example, we will assume you are developing your own engine to scan and transform the invoice into a structured data format. However, due to a lack of data, you will need to parse the Patent images dataset, which isavailableat http://machinelearning.inginf.units.it/data-and-tools/ghega-dataset. Within the dataset, there are images...

Summary

In this chapter, we covered how to extract data and provide AI services using APIs. We understood how important it is to manage customer's digital data. We also understood the Open Bank Project and document layout analysis. We learned about this through two examples—one was about projecting cash flows, while the other was about tracking daily expenses.

The next chapter will also focus on consumer banking. We will learn how to create proxy data for information that's missing in the customer's profile. We also will take a look at an example chatbot that we can use to serve and interact with customers. We will use graph and NLP techniques to create this chatbot.