Make your credit check results transparent without compromising security

The most common problem with the credit score today is that not everyone can interpret the model results quickly. A complex algorithm examines dozens of factors in dynamics and almost no one can directly see and understand what it is. The article describes an algorithm that uses natural language to explain a company’s strengths and weaknesses that influence decision-making. Credit will also not get you to the black list.

Why is a bank needed? Increase the speed and culture of corporate communication and effective dialogue with the customer. Banks had to implement new solutions retrospectively and quickly, which cost them a credit market share that was taken over by young and daring FinTech companies. A rating system is a black box in the eyes of ordinary people, and that fact lowers confidence in it. After a rejection, most clients help explain exactly what the bank doesn’t like and why the credit terms are so strict. The credit score is very important if you require a loan

If the bank decides to disclose the factor space used for the score, the fraudsters immediately receive an instrument for manipulating the key figures of the scoring model. All of this suggests that point models must be taught to communicate directly with the customer according to security requirements.

Apply the NLP pipeline scheme for scoring

NLP Pipeline is a scheme that more powerful chatbots like Siri or Alexa are working on. The algorithm can be divided into different key levels.

Step 1

In the first phase, there is speech recognition and the translation of sounds into symbols, words, and sentences. This phase is missing for the written speech. Of the mathematical models, deep learning is most often used in neural networks in this phase.

Level 2

The text document is then converted into a more machine-readable form using inference and lemmatization techniques. At this point, the system cuts out the suffixes and endings that make the language beautiful but do not involve any semantic burden. As a result, the text comes as close as possible to a machine-readable form.

It is believed that this level is highly dependent on the complexity of the grammatical structure of a language. However, this is only partially correct: Modern processors can also work with very complex languages and, despite their grammatical complexity, extract facts from the texts they contain. The analysis of a Hungarian or Icelandic text takes only a few milliseconds longer than a comparable analysis of an English text. However, the lack of libraries for analyzing texts in complex languages is certainly a serious obstacle.

level 3

The next step is to convert the text into tables using algorithms that implement formal grammar theory, such as B. the remaining z, not the entire grammar structure. An ontological analysis of the text is performed which transforms it into a number of formal constructs such as objects and subjects, properties, and methods; these are modification properties.

Level 4

Finally, the context and contextual meaning of the facts presented in the text are determined: it is an interesting phase that depends on the contextuality of the language and a particular text. Therefore, legal and other types of text are much easier to analyze than fictional works. As a result, the text is finally converted into a table in this phase, which is then inserted into the evaluation model.

The point model then processes the incoming data, tests, trains, and retrains. But most importantly, when you get the notes and based on which a decision has been made, the most interesting part begins: all the previous steps start repeating in reverse order:

1. The correct dictionary is selected based on the context.

2. The themes, genre, and inflection are placed; They form a sentence with the correct grammatical structure.

3. If necessary, natural language is synthesized, which interprets the result of machine learning processes.

The algorithm described above is the algorithm that automatically explains in natural language which weaknesses or strengths of a company or a person influence certain decisions. It is much better not only to get a rejection but also to find out what the main reasons were. In addition, it can reveal an error in customer data that can be quickly eliminated and leads to increased customer loyalty and higher sales.

In addition, by using this technology, employees do not have to explain how the points model works and why it works correctly.

Fraud protection

The question remains: How can the risk of disentangling the factor space and the complexity of explaining the dependencies between the factors be eliminated?

The high mobility of modern scoring systems helps here. Real-time learning technologies provide the ability to easily change the role of the factors influencing the final decision. This makes it unnecessary for scammers to hack into your system. When you build a business or a lender that meets the criteria you understand the importance of, the external environment and dot patterns outlined in it change, so all efforts will be in vain.

It is more difficult to explain nonlinear dependencies and how the role of a factor changes depending on other factors surrounding it. So far, a text document can only say something about the existence of such relationships, but it does not interpret it in natural language. However, technologies are constantly improving and everyone needs to closely monitor their development to be able to offer effective solutions to their customers.

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