To talk about this we have to think about the types and applications of machine learning algorithms, classify, predict, group, vision, etc. The main use I think of is to use classification algorithms to classify the client requesting a loan, to know if it is convenient to make a loan and the risk that it may entail. But another question arises. How do I assemble the model? It is necessary to choose the indicated variables and their probabilistic weights. Are work seniority, income, age variables that we must consider? In our favor, we have algorithms such as PCA, Rasso or Ridge to carry out this task and thus build efficient models. Okay, now you know that variables are used, but what groups will you classify customers into? This is a task that can be delegated to artificial intelligence and rely on algorithms such as k-mean or mean-shift to detect patterns that we, as humans, are not capable of perceiving. Making use of these algorithms has a great advantage since, they belong to unsupervised machine learning algorithms, this means that no labeled data is needed, which translates into an advantage for any financial entity.
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To talk about this we have to think about the types and applications of machine learning algorithms, classify, predict, group, vision, etc.
The main use I think of is to use classification algorithms to classify the client requesting a loan, to know if it is convenient to make a loan and the risk that it may entail. But another question arises. How do I assemble the model? It is necessary to choose the indicated variables and their probabilistic weights. Are work seniority, income, age variables that we must consider? In our favor, we have algorithms such as PCA, Rasso or Ridge to carry out this task and thus build efficient models.
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What about those loans that are for a project? Can they use artificial intelligence to determine the success of a project? Of course, they can use regression algorithms or decision trees, based on historical data from other projects, to obtain a probability of success of a project, to know if it is profitable or not, if it has a consolidated management team. (add company valuation)
The roots of the prediction can go even deeper, they can use their database of categorized clients to predict when is the best time to offer a loan, when a client will switch financial services, what is the probability that they will file for bankruptcy. and even in what period the client will lose his job source.
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Indeed there are many circumstances in which AI can be applied in such processes. As a starting point, we must take the very definition of artificial intelligence, since today it is a concept as transversal as it is confusing. John McCarty, developer of the Lisp language and winner of the Turing award, coined the term and it boils down to: “software that can solve problems by itself.”
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To that extent, financial institutions should apply AI at every step of the loan allocation process. One of the main tools within the field of AI is machine learning, which refers to algorithms that learn from data. With such algorithms it is possible to classify groups of users and predict future behavior.
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From the process of incorporation to the financial institution, these algorithms can be applied, for example in the interpretation of natural language to determine personality profiles and predict patterns of loan use. After approving the client, sentiment analyzes can be carried out and based on them, send promotions or define term extensions … or predict defaults.
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AI has come to the corporate world to stay and begins to be a decisive factor in the survival and growth of the institutions that employ it, since it will be a competitive advantage over other institutions, artificial intelligence has scopes that go beyond of process automation, with the development of deep learning and unsupervised algorithms the possibilities are unlimited, it would be interesting to consider how we could use neural networks to discover new uses applied to the entire financial world. It is a developing area that promises much more.
