Scrapers improve decision-making in the financial industry?

Financial industry How scrapers improve decision making in the granting of products 3

The importance of extracting user data

In our previous article, How to benefit from the capture of public data that exists on the internet, we talked about the importance of banks developing personalized financial products and services.

By establishing a relationship of trust, the customer feels at the center of the scene, enhancing the business model through a bond of loyalty to the brand.

These customization models, which make us more competitive, allow us to scale significantly and develop better services, are not the result of isolated processes.

They are fed by the same data that users generate and upload on the web (and then return to them with added value, every time they turn on their phone or open an email).

The driver of banking is, through data and analytics: anticipating the needs of its customers, targeting certain user segments and building deep relationships, based on trust, that last.

And this does not refer exclusively to the commercial process of selling a product, but also to providing a service, recommendation or information according to the specific need of each person.

The accelerated growth of websites that publish information every day has no brake. Capturing the right information, at the right time and with the right quality, is vital.

If your organization is already immersed in the information extraction process, we will tell you how you can face the most complex challenges that this massive data extraction entails.

We want to make it easier for you to make decisions and improve, even more, your offering of services and products.

What are the challenges of the data extraction process?

First of all, we must know what information is relevant to extract.

The data is displayed everywhere.

Having specific objectives is very important to know how to maintain a cost-effective scheme that allows us to collect what is necessary and bring new products in a competitive time frame.

Demographic information, or data on user needs, tastes and consumption patterns, allow us to offer promotions according to each segment and increase the feeling of belonging.

Santander UI, for example, detects the activity of young users to offer them discounts in places they frequent.

A simple like to a publication can provide a lot of information about the consumption pattern of users and, thus, establish relationships between them, allowing to generate recommendation systems that are quite successful.

In our previous article we told you how the information that users produce, or through which they interact with others, can be found in a structured or unstructured form.

Structured information is easier to interpret or use. Rather, a more dedicated healing and transformation process is required to extract the value that unstructured information contains.

One of the most challenging tasks in extracting public data from the Internet is the high heterogeneity of sources: we cannot generalize the information we extract. Not even when we get it from platforms with similar services (two social networks, for example).

Obtaining permanently updated data is another of the most difficult challenges to face in this data extraction process.

This need, added to the enormous volume of information that is produced, forces us to keep the extraction processes in a state of constant observation and monitoring.

Continuous adaptation to new representations becomes essential in data normalization processes.

Our clients demand to receive up-to-date products and services with a quality that is not surpassed by the competition.

A marketing campaign, for example, must be fed up-to-date information because, nowadays, the volatility of social networks leads the recipients of my campaign to lose interest quickly.

Precarious automations or manual removal process: Why not?

The volume of information available to offer financial products and services is extremely abundant. Hence, we talk about “Big data”.

Downloading the information manually would reduce the scope that is obtained automatically and would prevent us from constantly updating as expected.

Automation is a must to offer products that add value to the lives of users.

But this automation cannot be simple or precarious because it impoverishes the representation of information and limits updates.

Using simple robots to download the data becomes insufficient.

The scalability challenge

In the event that more and new sources of information emerge, as experts in Web Data Extraction we must be prepared to scale and obtain new and better results.

We must also scale when sources under extraction increase the volume of information they make available.

In that case, it is strategic to have the means to solve this challenge quickly, without losing sight of the fact that we must maintain redundancy or maintain a fail-safe system.

In short, you have to know how to attack all these variables, which can potentially appear (and in fact, it happens all the time).

How? Taking into account the following:

Efficiency: are we downloading the information in a timely manner?
Consistency: is the downloaded information aligned with our vision?
Reliability: Does the data come from reliable sources?
Quality: do they meet the mission objectives?
Monitoring: are we downloading updated information?
Adaptability: are we ready to absorb change should it arise?
Scalability: are we ready to add more sources or include more information?

The importance of using experts to build models on secure, scalable and available platforms

A major bank in Ecuador chose the 7Puentes Web Data Extraction service. Our team offered you a varied portfolio of solutions that made it easier for you to make internal decisions.

Prior to our arrival, a team of 30 people manually controlled and downloaded the credit information of different clients, corporate and individual.

Today, this team is made up of two people who validate and control the information delivered with well-defined quality criteria.

At ScrapingPros we have the experience and the team of experts necessary to face any challenge, large or small, to make your bank grow.

We have a platform prepared to meet the requirements of processing large volumes of information (efficiency, consistency, reliability, quality, monitoring, adaptability and scalability), working side by side with our clients.

We determine the best solution to face the set objectives and we obtain results in an agile way (iteratively and incrementally).

Find in our Web Data Extraction service the best way to automate the extraction, updating and standardization of information from potential or current clients to improve decision-making in your organization.