The banking business is at a standstill. To be clear I am not talking about the current problems like capitalization and bad loans. I’m talking about the banking business itself. Let’s see what is going on.

As in the recent past happened to the book business (Amazon), to music (Spotify ) and more recently to movies and TV (Netflix), or as is happening with the individual transport business (Uber) or hotels (Airbnb), the banking business is under attack from the digital transformation of the economy.

The so called Fintechs, companies that now provide a range of financial services based on the digital transformation of financial processes and interfaces, are actively taking up more and more of the space that banks have occupied for the past century And they do it at a fraction of the cost that traditional banks have, because by using the new digital approach, they relocate, simplify and eliminate the middle man.

In fact, the major barrier preventing Fintechs from taking more banking business is legal. Is the legal framework that allows data in a bank’s computers to represent money. It’s because of this, that banks are able to complete business transactions between economic agents.

In addition to Fintechs the banking business is also under attack from social networks. Crowdfunding (obtaining financial funds to develop projects through the participation of thousands of people) now allows you to create business without resorting to bank financing or purchasing financial advisory services offered by investment banks. The most popular platforms are Gofundme, Kickstarter, Indiegogo, RocketHub, Fundrazr, Gogetfunding and Crowdfunder.

There are two basic reasons for this broad attack on the banking business. One is the knowledge of the business, which after all these years, is well structured and defined. The other reason is the emergence of technologies that automate this knowledge.

Before proceeding I will introduce the concepts of a structured, semi-structured and unstructured problem.

• Structured problem: in this case we can represent and solve a problem using a group of known data and processes. This type of knowledge is represented by systems (data, algorithms, machines), mathematical equations, architectural plans, engineering projects, etc. A concrete example is the ATM, or in a broader concept, the payment system. These problems are solved by deterministic systems.
• Semi-structured problem: this exists when we can solve part of the structured problem, but there is a part of a group of data that is unstructured and we need to use statistical models to reach a conclusion. Examples: public opinion polls, or decision support systems. These problems are solved using statistical models.
• Unstructured problem: when there is no model, either deterministic or statistical that can solve the problem. Crime is an example of this kind of problem. There is no model that solves crime, either deterministic or statistical. There are a number of tools to address crime, but that does not prevent it from happening.

In summary, structured problems are solved by structured knowledge (deterministic models), semi-structured problems are solved by semi-structured knowledge (statistical models) and unstructured problems are not solved until it’s possible to model the problem.

Let’s see what is happening with the banking business:

• All structured knowledge can be represented by data and algorithms, making decisions in automated processes.
• The semi-structured knowledge can also be represented by the new technologies of Big Data (statistical databases) and predictive statistical algorithms with a high degree of confidence.
• Companies based on structured and semi-structured knowledge as retail financial business, tend to be fully automated.
• This automation doesn’t need branches and people to provide financial services and meet customers needs.
• Banks, which are financial intermediaries, are being attacked by the digital economy, with an almost “do it yourself” approach.
• Operating banking capacity, based on the banking technique of its employees and current information systems will be replaced with equipment, databases and algorithms.
• The commercial banking skills of bank employees will be replaced by Artificial Intelligence techniques:
  • Predictive algorithms that anticipate customer needs and
  • Interfaces that will understand human behavior
• The bank of the future will be a set of databases, statistical databases, algorithms and equipment that interacts intelligently with customers.
• The bank of the future will be essentially a fixed-cost business, where the marginal cost of generating one more operation will tend to zero.
• The value of a retail bank will reside:
  • In owned algorithms,
  • In owned databases and
  • In capabilities to access Big Data, owned and generated.

So to win this battle, banks need to assemble teams to define and implement the critical algorithms for business in several dimensions:

• Interface with customers and between systems;
• Operational support for customers  throughout their life;
• Forecasting the needs of each customer;
• New generic products that adapt in real time to each client

It is clear that the banks that lack these capabilities have no future. And they have to adapt now, while they are still protected by the legal system.