How AI changes the world of web scraping in finance

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As a network of geo targeted proxies, Dexodata is a trusted partner of finance entities. They apply our IPs for web scraping, stock info collection, etc. Based on this, we accumulated knowledge on finance dataset extraction and new roles for AI in this. In case you are engaged in this professional field, you will see our findings relevant. 

Finance data landscape

Back in 2010, the amount of data in circulation was 2 zettabytes. Soon, in 2025, it will exceed 181 zettabytes. If one thinks of data collectors, analysts, managers — we at Dexodata do so as vendors of geo targeted proxies for info gathering, these professional groups are our target audiences — one will become amazed. How could it be possible to process this sheer scale? This question is doubly true in finance.

Money is all around. So, theoretically, any piece of info pertains to cash. Social media sentiment, celebrity endorsements, hype posts might be as important for finance entities as purely economic indicators. This resource, for instance, gives access to 20 million economic metrics covering 196 countries, e.g. indicators, historical data rows, charts, news items, forecasts alone. Surprisingly, data experts, retrieving that abundance through buying residential and mobile proxies, as well as datacenter IPs, perform well under pressure. Thanks to web scraping supported by AI. 


Finance web scraping trends


As a provider of geo targeted proxies fully compatible with AI tools, we present ongoing trends in a table. 

Increasing data usage Given how often teams from the finance sector buy residential and mobile proxies, our estimate is that data usage in this domain grows by 30% annually. For keeping up with this pace, web scraping gets ever-more automated, which necessitates rotating IPs
Reliance on alternative data Firms use alternative data (social media posts, news articles, satellite imagery, transaction data) for insights beyond traditional finance items
Named Entity Recognition (NER) NER, part of alternative data reliance, identifies, categorizes, assesses key information pieces in texts (entries such as names, organizations, locations), extracting relevant insights from vast datasets
Multilingual web scraping use cases Web scraping is expanding, encompassing multiple languages, allowing firms to tap into diverse data sources globally.


Challenges posed by web scraping trends in finance 


Adapting our geo targeted proxies to altering market conditions, we hear three chief concerns from IP purchasers:

  • Cleaning scraped data to make it usable, addressing noise, inconsistencies, errors
  • Harmonizing data from diverse sources for coherent scrutiny
  • Ensuring that data collection practices are ethical, compliant, clean.


Reactions from finance industry players 


Users buying Dexodata’s residential and mobile proxies give a range of answers to these pressing concerns:

  1. Automating data collection through AI. This streamlines info handling routines by managing web scraping procedures, data cleaning, correcting errors, normalizing formats, enhancing row quality, reducing manual intervention, and speeding up workflows.
  2. Ensuring ethics and full transparency in web scraping, coupled with data usage, to avoid ethical and legal issues.
  3. Investing in resilient web scraping finance software to provide for adjustment to web access policy changes and reduce redundancy through regular updates, monitoring, etc.
  4. Allocating resources for advanced NER tools to boost precision in extracting finance info by training models on diverse datasets, refining them through feedback, enhancing accuracy.

These four patterns are costly, labor-intensive, urgent. However, there exist no alternatives, due to the emergence of enormous datasets and highlighted web scraping trends. To match them, Dexodata offers a global pool of residential and mobile proxies to buy, so finance experts can work on a potent, solid, and ethical foundation.


Data gathering made easy with Dexodata