QxBranch delivers predictive analytics solutions with a focus on explainability and transparency

QxBranch brings expertise in data science, machine learning, and visualization, pioneering explainable solutions for the most complex problems.

How We Engage

From strategy and consulting to scalable data-driven software solutions,  we help clients maximize the potential of their data

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Assess & Strategize

QxBranch works with businesses beginning the analytics journey and provides guidance on how to make the most of the data they already have through targeted workshops and data strategy consulting, data assessments, and talent strategy

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Analyze & Validate

QxBranch works with stakeholders to identify pain points across the business and develops actionable data science hypotheses for investigation through exploratory  analysis, proofs of concept, and Analytics as a Service that flexes with business needs

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Scale & Deploy

QxBranch combines the art of data science with the rigor of software engineering to develop customized and scalable data science solutions which generate insights and gain efficiencies across the business

Solution Offerings

QxBranch offers explainable data science solutions for complex, high-value challenges which enable model validation and improvement, satisfy regulatory requirements, and address algorithm and data bias.

Pricing
Risk
Behavioral Analytics

Pricing

Optimize pricing decisions and uncover insights hidden in market data

Case Study: Consumer Behavior in Response to FX Pricing Fluctuations

A multinational banking and financial services institution sought to maximize foreign exchange (FX) product profitability using predictive analytics to derive competitive pricing strategies based on consumer behavioral patterns, macroeconomic insights, and market trends.

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Challenge

  • Disparate data sets required assessment and cleansing to address high levels of missing data and to  eliminate data errors
  • Complex data wrangling required collaboration between analytics experts and domain specialists to derive methodologies to transform the data while retaining relevant information and data quality
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Solution

  • Utilizing a machine learning clustering algorithm, customers were segmented, and profile types were identified
  • Factors driving customer behavior to price fluctuations were determined for actionability
  • FX product demand curves were calculated for each customer profile
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Outcome

  • A data-driven recommendation system was deployed, providing the optimal pricing and transaction margin for FX products by customer profile type
  • The system also assessed the likely impact of changing market conditions and profitability by customer profile type

Risk

Detect and assess risk utilizing advanced machine learning algorithms

Case Study: Risk Quantification for Cyber Threats

Recognizing the limitations of traditional underwriting methods for cyber insurance, a global insurance company sought to develop a robust and novel methodology for cyber risk quantification using diverse data sets and advanced machine learning techniques.

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Challenge

  • Data is difficult to obtain, incomplete, and unreliable due to the sensitive nature of cyber risk
  • Threats are sentient and shifting, with risk mitigation techniques and threat tactics continually developing
  • Current risk assessment tools do not consider broad measures of cyber risks
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Solution

  • A holistic approach to cyber risk analysis was derived by decomposing complex problems into constituent vulnerabilities, threats, and assets
  • Novel machine learning algorithms were utilized to capture market dynamics
  • An adaptive model was developed to flex and evolve with the changing cyber risk landscape
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Outcome

  • A dashboarding solution was developed and deployed which provides explainable risk quantification outcomes and results that enable underwriters to properly validate and file rates
  • Cyber risk is captured and quantified for accurate pricing of insurance policies and reinsurance contracts across multiple coverage types

Behaviorial Analytics

Identify customer behavioral patterns and perform sentiment analysis

Case Study: Market Predictions using Social Media

A financial research and analysis company wanted to determine the predictive power of certain social media applications on stock market activity and trends.

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Challenge

  • Insights and trends are difficult to identify and isolate in social media and financial data due to high content volume and levels of “noise”
  • Social media posts and other text data require contextual knowledge and modeling to properly assess sentiment and other insights
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Solution

  • Natural language processing based on custom, domain-specific algorithms was used to classify sentiment and other variables potentially linked to market activity
  • Additional factors, such as the social media post timestamp and related market activity, were utilized to derive insights
  • Predictive models were derived to identify leading indicators of market activity
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Outcome

  • Signals and insights were generated ahead of related trending market activity for small- and mid-cap stocks
  • High volumes of data were transformed into actionable insights for use in decision-making by analysts

News

QxBranch Named to “Tech 25”

Washington Life magazine named QxBranch to its 2015 “Tech 25” list. The November “Innovators” issue focuses on Washington D.C.-area entrepreneurs who are “making waves, affecting communities, and simplifying

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