| Tech Blog
| Artificial Intelligence, Machine Learning and Big Data: the new business paradigm
Artificial Intelligence and Big Data are concepts that resonate more and more because they transversally cross all organizations. These two practices place data at the center of the scene, as the way to transform and optimize any business process.
ARTIFICIAL INTELLIGENCE HAS A GREAT POTENTIAL AS AN INFORMATION SOURCE:
- It processes images to detect objects, identifies people and generate metadata.
- It processes spoken language and its generation. And it’s able to understand the natural language to evaluate feelings and topics.
- Learns based on historical data to generate knowledge and predict the future.
The ability to optimize and automate decisions, based on real-time data intelligence, allows a radical competitive improvement to companies from all sectors of activity. Especially in the financial business, where it has helped to apply the enormous potential of these technologies and has put them to service of each objective.
WHICH ARE THE ADVANTAGES OF APPLYING AI TO THE FINANCIAL BUSINESS?
- Reduces risk as it provides information about users for decision making: credit approval, control of investments, fraud detection, leakage prediction. This information is also used to identify money laundering.
- Generates added value by customizing services and optimizing resources.
- Improves customer service, offering the possibility of including a virtual assistant and analyzing user’s opinions and feelings.
- It facilitates the detection of new leads, up and cross sell.
Increasingly, data is the oxygen of organizations. In this context, it becomes essential to know the different sources from which to obtain these data. In fintech applications data comes mostly from internal systems where you can get customer data, transactions and information, websites and mobile applications that collect behavior, platforms that allow interaction with clients such as social networks, calls and emails sent to customer service.
HOW IS THE WORKFLOW?
Which are the different stages in a project with these characteristics? First you must understand the business to define the analytical approach that accompanies the pursued results.
Once the process is defined, data collection begins either through social networks, internal systems or applications, as suitable for the project in question.
Once the information has been collected, the unstructured data are prepared, processed and modeled, they need to be assessed to turn them into valuable information for the client. Afterwards they are processed by different suites for its usage.
The potential of machine learning technologies is inexhaustible, especially in financial institutions because it allows them to update and stay competitive in an increasingly diversified market.