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Learn Python for Free with Machine Learning Applications Using Python

Cases Studies from Healthcare, Retail, and Finance

Machine Learning Applications Using Python

Overview of Machine Learning in Finance

A few years back I was approached by the financial client from the Southeast Asia region to help them with their machine learning effort since they were newly implementing it in their industry and they had become stuck with the practical implementation of the machine learning algorithm in their financial advisory services domain.

During the implementation, I studied the financial industry around the world in order to get a better grip on what was required in order to implement this assignment. Even today the implementation of machine learning and artificial intelligence in the field of the financial industry holds a lot of scopes.

In a very broad sense, financial services, as defined by Wikipedia, is comprised of businesses that manage money. These include credit unions, banks, credit card companies, insurance companies, accountancy companies, consumer finance companies, stock brokerages, investment funds, and individual fund managers.

Of course, in our discussion, I am not going to talk about the government-related financial services.

As far as the segmentation of the financial services market is concerned, I will be using the following broad segments that are described in Table 13-1. The clear differentiation in the business approach can be seen from Table 13-1.

Machine Learning Applications Using Python Table 13-1

In Table 13-1 the market segmentation of the financial services industry, which is based upon the financial services industry, subprime and credit crisis weighs heavy, according to a 2008 edition report by John W. Molka III, CFA Senior Industry Analyst and Editor.

We first look at the consumer finance section, which is a group of companies providing personal loans, indirect credit card issuers, pawn shops, and payday loan providers. This list does not include mortgage and lending companies. The main item that these companies deal with is unsecured loans to individuals.

Of course, all the statistics given in the report are based on the US; however, the percentages are applicable for all the developed countries. For example, the consumer finance companies hold 23 percent of the consumer credit as of 2007, and this can be applied to other developed economies as well.

While this statistic looks dated by more than 10 years, it gives us an idea of how much share the consumer finance companies hold in the consumer credit in a nation’s economy.

Next in the segment are capital markets, which are comprised of organizations undertaking stock trading brokerages, strategic financial advisory portfolio management, asset management, and professional investment advice. These companies are intermediaries, and they work for the goals of other financial institutions or individuals.

These companies include investment banking and trading in stocks, bonds, derivatives, and commodities. It also includes asset management firms that manage large funds for individuals and institutions around the world. The increasing trend in the sector is the rise in investment banking and securities dealings and that of securities brokerages.

The largest services category is investment banking and security dealing, which comprises 41 percent of the total segment business. An increasing trend in the segment is the rise of mutual funds and investments, and this trend can be seen both in the developed and emerging markets.

Which is essentially the companies involved in financial advisory investment research, stock exchanges, rating, and credit rating. This is a market where there are top 10 players, like Citigroup, Berkshire Hathaway, Goldman Sachs, Morgan Stanley, JP Morgan Chase, and Merrill Lynch. The next is the diversified financial services segment.

Now that we have looked at the broad segmentation in the financial industry, we will now look at some of the key segments and the value proposition that they provide.

Machine Learning Applications Using Python Figure 13-1

The banking segment is comprised of consumer lending, which is a very structured form of business where the creditworthiness of the consumer is ascertained through the creation of credit profiles of the consumer. If the credit profile is found to be risky, then the rescue level is ascertained, and only then is the lending possible in the segment.

Next is the business lending, which is also a very structured form of lending to commercial organizations for their investment and growth. Although this type of business lending also has a lot of variations, I am not going to go into them now. The third type of banking segmentation is that of e-payments. Both the consumer and the business make electronic payments through the electronic payment banking systems, which are local to each country.

The consumer lending can further be divided into credit card lending and secured loans lending. Credit cards are issued by banks only when the creditworthiness of a person is ascertained so that they can perpetually use the credit and then repaired within the given stipulated period. Secured loans are given only when the creditworthiness of an individual is ascertained with respect to the assets that they hold and the liabilities that they have against various loans that they might have taken in the past.

In the non-banking space, we divide the segment into advisory services and the stock markets. In advisory services we again divide our segment into credit advisory services and investment advisory services. The credit advisory services are related to the credit ratings of agencies, banks, countries, and companies.

There are various credit rating agencies that operate all over the world and they fall in this segment category. Investment advisory is concerned with management and advice on the funds held by various individuals and organizations. This includes portfolio management services and other such financial advice given by various agencies.

Stock markets involve a segment of portfolio management services and brokerages that can be the regulated stock exchanges and unregulated stock exchanges, like the ones used by blockchain technology (e.g., bitcoin). We will be referencing this drill-down version of our segmentation of the finance industry in this finance section throughout this book.

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