Big Data
Analytics

Creating value through data

Common Big Data Applications

  
Fraud – Identify patterns of fraud and use them to audit events more exposed to fraud in order to prevent its occurrence (predictive analysis).

Profiles or “clusters” – Cluster clients and other audiences into groups according to various behavioral features, such as profitability and consumption profile, so as to make more secure investments and specialize company processes according to these profiles.

Propensity – Target clients most apt to take a given offer, on a given channel, at a given time.

 
Big Data Analytics- Creating value through data
 

Companies already have too much technology available. Focus on integrating IT, Operations, Marketing, Sales to turn data into value.

BRQ helps a company’s business and operations areas to use their available data and technology to improve decision-making processes.

Most companies have huge volumes of data and an abundance of technology, but its use is often poor, falling short of expectations. Business and operations teams have to work together to identify the best opportunities to build and enhance their models using the available data and technology.

  • Specialized consultants join experts in such fields as technology, math, and statistics;
  • Vast knowledge of business and operations in many industries;
  • Full management of the data portfolio and existing applications in many companies;
  • Expertise in a wide range data technologies.

 

Case

One of Brazil’s leading auto insurance providers

All insurance providers face two challenges in their claim processes to remain profitable:

  • Fraud, whereby holders and service providers act to obtain benefits in disagreement with the policy taken out.
  • Offering clients quality repairs that are both efficient and cost-effective.

Using Big Data / Analytics methods, BRQ helped the insurance provider review the quotes from auto shops and optimize the auditing of budgets by creating mathematical models based on the company’s claims history.

 

Other experiences

  • Development of methodologies to assess interbank deposit rates.
  • Development of algorithms to calculate Market Risk, Liquidity, and Credit for FDICs (Credit Rights Investment Funds) and other investment channels.
  • Modeling Credit Risk analysis to support policy pricing decisions for large construction projects (Growth Acceleration Program – PAC).
  • Creating algorithms to detect deviations in losses involving perishables and to quickly establish corrective actions.
  • Modeling to assess the return and risk of credit card issuers’ new clients.

 

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