In the age of the Internet, the massive availability of information offers companies a great opportunity to improve the knowledge of their customers and prospects, offering in return products designed exactly to suit them.
However, the massiveness of the data hinders the efficiency of use: the information is there, but companies cannot process it manually, wasting a lot of potential.
To automate the extraction of information there is the Web Data Extraction, also called Web Scraping. Combining technology and specialized knowledge, companies turn to Data Science to exponentially expand their ability to access any website and extract invaluable data on a massive, orderly and quality scale.
One business sector that is taking great advantage of Scrapers is that of banks, fintech and financial institutions.
The power of automation
The financial industry knows the value of knowing its clients better. Innovating with tailor-made products is, increasingly, a matter of survival for the fierce competition with which banks coexist, whose main input is, almost exclusively, information.
The credit risk, sales, operations and marketing departments burn their tabs to find a way to be more efficient in analyzing the preferences, history of actions, consumption and payment capacity of their current and potential customers.
However, resolving data access and processing internally and with the highest efficiency demands specialized knowledge that takes time and resources from software development or IT departments.
Banks turn to Web Scraping to solve their needs to capture information in an automated way, perfecting their knowledge of their clients’ preferences and their consumption patterns without major restructuring of their processes.
How is information extracted from the Web?
Let us suppose that the bank identifies as a key source of information the state agency that handles population censuses at the national level.
First, robots called crawlers enter the website of this body and obtain the billions of raw data from its website.
The robots then extract the attributes that, let’s assume, the credit risk department defined as critical to its annual goals. Finally, based on a set of defined rules, the different types of content (text, numbers, links, images, etc.) are downloaded.
These collected contents are easily integrated into the internal information that the bank already manages, exponentially enriching the analysis of the clients’ financial statements, by incorporating a large number of qualitative and quantitative criteria that will predict with greater accuracy, for example, the capacity of a customer to cancel a credit with the bank.
In this way, the risk department can offer increasingly personalized credits based on the deep knowledge it learns from its clients, in a process of constant improvement.
By incorporating Web Data Extraction, banks improve their value proposition and increase their closing rates.
And all this, taking advantage of public information that is available to all.
The uses of Web Data Extraction
If we mention the word “data”, the first thing that comes to us is a cataract of hard, quantitative data, running in the format of zeroes and ones on an information highway, like an emblematic scene from the movie The Matrix.
The interesting thing is that Data Science enables, today, the possibility of processing not only quantitative information, but also qualitative information. For example:
- Analyze the competition: through Web Data Extraction it is possible to evaluate, for example, the efficiency of digital marketing campaigns and social media guidelines, detecting how they impact users.
- Sentiment analysis: “Moods” of users can be extracted from publications made, services or products launched. In this way, web scraping allows you to know if the campaigns launched by a bank are positive or negative and, thus, apply the appropriate corrective policies.
- Market research: Open the knowledge of consumption habits, needs and preferences of customers and prospects. It also allows us to understand aspects to be reinforced in marketing campaigns and to detect new market niches.
- Data Enrichment: It allows to generate products, notes and documents of different media to centralize the information for its later consultation or use.
What sectors of the financial industry are fueled by data science?
The main departments in a finance organization that use Web Data Extraction projects are:
- Sales: The combination of demographic information with data on consumption, habits and product preferences allows customizing the commercial proposal. For example: personal loans or credits can be offered to clients detected with the intention of making large purchases (real estate, cars), while those with travel plans can receive international credit cards or traveler’s insurance.
- Marketing: Through Data Extraction you can know the impact of a product or service launched from the opinions and feelings transmitted by users. In the same way, it is possible to anticipate the success of a campaign, revealing the expectations and needs of customers.
- Product: A Web Data Extraction project can offer information systems that group disorganized data in multiple places, making it easier to consult.
How does a financial institution start the implementation of Web Data Extraction?
The Web Data Extraction process includes a series of steps that must be carried out to achieve the expected results:
- Determine scope: The first step is to determine what is the objective you want to achieve and what will be the chances of success based on the information available. Once the variables to be considered are known, the selected website is interacted with to find out what can be extracted.
- Crawling: Once the scope is defined, the crawlers that will download the content of each site are defined.
- Scraping: When the raw content is ready for extraction processing, the paths of the attributes to be downloaded are obtained.
- Standardization and cleaning: Many times the extraction of information is done from different places. For example, obtaining the age of a person to determine if they can be offered a credit card could be extracted from a social network. The point is that not all people use the same social networks. For this reason, it is essential to perform data cleansing, which involves cleaning and normalizing the information so that it becomes orderly and legible for use.
- Planning: Today the flow of information is so abundant and dynamic that it can become out of date very quickly (new users, updated profiles, modified prices, etc.). For this reason, a continuous crawling process (obtaining the raw information) is necessary, which captures all the changes that may be generated.
- Maintenance and monitoring: Websites constantly update their content and the way to organize it. For this reason, monitoring is key to doing Web Data Extraction: it is necessary to be able to recognize when a site has changed, to execute the updates of the scrapper rules and, thus, to resume the correct download of the content.
ScrapingPros is an expert in Scrapers projects
At ScrapingPros we work for large clients in the financial industry, implementing Web Data Extraction services that impacted on the innovation and growth of these companies.
We are a team of scientists and engineers, lovers of challenges, specialists in data science.
We offer a comprehensive solution, which includes all the processes that encompass the Web Data Extraction practices. We are able to survey, categorize and model the sources of information available on the Internet, adding value to companies through easy integration with their own systems, without depending on us to take advantage of them.
Together with our clients, we determine which are the most relevant objectives and we generate an action plan that allows maximizing the obtaining of results, that these are of continuous improvement in an incremental and agile way to their products / services.
Our developers analyze the sites, determine which challenges are presented in each case, and thereby understand the best way to download the desired information with the highest quality. We work with our own data extraction platform that allows us to browse third-party websites anonymously and safely.
We include an important and intensive QA process that allows detecting and reducing the time it takes to update or readapt the crawlers to changes in the sites and, thus, resume all processes in the fastest and most efficient way.
We have developed Web Data Extraction solutions to:
- Products from e-commerce sites
- Articles and notes on news portals
- Documents from different entities (governmental, business, judicial, stock exchange, etc.)
- Tax information
- Financial statements, balance sheets of shareholders and companies
- Extraction of information from social networks (Twitter, Facebook, Instagram)
We have extensive experience and a highly qualified extraction and monitoring team, which has been perfected project by project, in response to the different needs of our clients.
Your financial institution is our next challenge. Do not hesitate to contact us if you are looking to benefit from the intelligent capture of public data from the Internet.