Data Science in Financial

Best credit classification and fraud prevention

Big Data in the Financial Industry

The speed, variety and volume of data associated with the financial market has grown at an impressive rate. Activities such as social networking, mobile application transactions, server logs, market data in real-time, transaction data and metadata, investments, and so much more.

With a digitalization services trend, this is only the beginning. To benefit from this huge amount of information, large companies are investing in Big Data and hiring and preparing the so-called data scientist professionals. These professionals must be able to:

  • Capture and analyze lots of data sources, generate predictive models and simulate market events.
  • Using technologies like Hadoop, NoSQL and Spark, identifing and integrating unstructured data (eg, social networks and sensor data analysis) with structured data.
  • Find and store increasingly diverse data in its raw form for further analysis.

Practical Examples

Sentiment Analysis

Sentiment analysis applies NLP techniques (Natural Language Processing), text analysis and computational linguistics to evaluate what customers think of your company.

  • Development of algorithms that can monitor social networks and specialized media, identifying the occurrence of events that could indicate the need to dispose of certain investments;
  • Track trends, monitor the launching of new products, answer questions and improve overall brand awareness;
  • Analyze unstructured data from call centers and recommend ways to reduce customer churn, up-sell and cross-sell products and detect fraud.

Automating Credit Risk Management

Thanks to providing a wealth of information on the Internet, a new generation of financial companies (known as fintechs) are finding different ways to approve loans and risk management.

  • To assess whether a particular company is a good payer, you can collect data from their e-commerce platforms, customer reviews, shipping records and a host of other information.

Analysis in Real-Time

Until recently, financial institutions were affected by the delay time between data collection and data analysis. With real-time analytics, this issue is addressed. Moreover, it offers new ways of working.

  • Combating financial fraud: Banks and credit card companies can routinely analyze account balances, spending patterns, credit history to determine whether the transactions are outside the standards. If suspicious activity is detected, they can immediately suspend the account and alert the owner.
  • Improving credit ratings: Continuous online transactions of data means that credit scores can be updated in real time. This gives lenders a more accurate picture of its assets and business operations.

Costumer Segmentation

Like any other industry on the planet, banks and financial institutions have the need to know more about the people who use their products and services. With Big Data you can use various tools to create a 360-degree view of your customers.

This type of customer segmentation allows them:

  • Offer personalized product offerings and services
  • Improve existing profitable relationships and avoid customer churn
  • Create better marketing campaigns and products more attractive offers
  • Development of products tailored to specific customer segments
  • And much more...

To help your business data leverage your business results, Semantix's team has several qualified experts to implement a complete solution for Big Data from the infrastructure state up to the data ingestion and analytics in real-time with complex algorithms, such as Machine Learning techniques.