APPLICATIONS FOR UNUSUAL DATA

NEXT LEAP FORWARD IN DATA INTELLIGENCE

Data Science is delivering the next leap forward in the evolution of business intelligence. Together, we convert data into structured knowledge and develop machine learning systems to process and act on that data effectively.

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Data Science Applications

> Data Mining

> Contextual Optimization

> Visualization

> Rules Based Decisioning / Automation

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CASE STUDIES

New and unusual data types were required to open new credit market.. MORE

Text mining public info to gain insights and actionable data for compliance... MORE

APPLICATIONS ▼

Machine learning: driving the evolution of business intelligence...

> Identify and Acquire Data

> Scope of Data

TECHNOLOGY ▼

What can be achieved ?

New models of efficiency can be realized. Response to opportunities can be measured in minutes and seconds, rather than days and weeks. Predictive systems to identify and highlight important patterns and trends. Reports that used to require analysis can be delivered as dashboard visualizations, achieving contextual relevance at a glance. Business units can be united behind the effective utilization of shared data sets. Security, accuracy and compliance can all be optimized.

Transaction speed can be improved at orders of magnitude greater efficiency, with systems that are secure and scalable. Human error can be replaced with uniform reliability and perhaps most importantly, people can focus on creating new opportunities and on high-value activities rather than mind-numbing repetitive actions and processes.

Value of Machine Learning in

Financial Applications

Machine learning is the science of designing algorithms that are able to learn and act on their own data. From beginnings in data science for pattern recognition, machine learning has emerged to enable true predictive learning. Systems are able to respond to a range of situations even though not being explicitly programmed for each eventuality.

Our work centers around creating efficiencies, improving accuracy and reducing risk in ways that are automated, scalable and ultimately dependable.

The need to integrate seamlessly with marketers, underwriters, financial analysts and other professionals will force machine learning platforms to do more than just pump out predictions - they will have to drive results.

Cross-organizational Integration

Reduce mistakes and human error by automating compliance.

Systemization of rules-based actions and definition of mandatory compliance criteria.

COMPLIANCE

ACCURACY

Machines can perform repetitive tasks for an infinite amount of time. Machine Learning algorithms carry out rigorous data analysis and flawlessly execute on tasks, escalating decisions to humans when needed.

Machine Learning helps to secure systems and processes by building models which can predict fraud or errors through anomalies and patterns in data and between behaviors of identities.

SECURITY

Applications of data intelligence can result in unexpected network effects and interconnectedness. The flow of data between offices, divisions, markets, partners and institutions can be harnessed in ways unforeseen and with orders of magnitude greater efficiency.

NETWORK EFFECTS

Transactional speed is essential. Machine Learning algorithms are able to provide accurate in-depth analysis of thousands of datasets in a fraction of a second. Tasks can be sped up, responsiveness enhanced and opportunities maximized.

SPEED

In financial services, trust is essential. Rules based systems and regulations are adhered to explicitly. Deviations are flagged and sequences of actions methodically followed. This is delivering reliability as rules-based standards.

RELIABILITY

Optimization of capital and personnel resources through automation. Executing repetitive tasks that are prone to errors, streamlining processes and effectively directing high-value tasks to the right people.

EFFECTIVE

COST REDUCTION

APPLICATIONS

Risk Management
Machine learning models are trained with sets of data used to predict the probability of fraud. Patterns and anomalies are detected moving forward based on all known points of reference. By comparing transactions against account history, machine learning algorithms are able to assess the likelihood of transaction being fraudulent or erroneous and to better predict outcomes with more granularity and relevance than just credit scores and static reports.
Customer Service
Optimization of resources for customer service can be effectively optimized through machine learning. Beyond the universal utilization of automated phone support, we can find the points of frustration and direct the right responses - even beginning to anticipate situations where customer retention may be in jeopardy.
Predictive Analytics
Following trends, patterns and flows of transactions can enable many levels of efficiency towards accurate, actionable predictive analytics. Knowing the likelihood of an event can impact investment timing, data utilization and optimize use of high cost personnel engagement.
Automated Interaction / Digital Assistants
Machine Learning technologies include functionalities that can be useful for developing custom process automation and digital assistants. Assessment of data, analytics capabilities, speech recognition and the ability to interact with relevancy is rapidly evolving. The ability to incorporate these efficiencies is evolving as a core capability in terms of a competitive landscape that increasingly dependent on streamlined low cost services.
NEW PRODUCT OFFERINGS
Machine Learning technologies include functionalities that can be useful for developing custom process automation and digital assistants. Assessment of data, analytics capabilities, speech recognition and the ability to interact with relevancy is rapidly evolving. The ability to incorporate these efficiencies is evolving as a core capability in terms of a competitive landscape that increasingly dependent on streamlined low cost services.
NETWORK SECURITY
Identifying suspicious network activity can be the most effective way to get ahead of critical network security issues. The power of intelligent pattern analysis, combined with big data capabilities gives machine learning technology an edge over traditional tools.
LEADING EDGE TECHNOLOGIES
There are numerous platforms and systems that can be adapted to the unique needs of a specific business unit or initiative:

Robo advisors
Blokchain and hashgraph solutions for immutable data validation
Alternate payment options
Interconnections for global capital markets
ASSESSMENT OF CREDIT QUALITY
Aquiom has been a leader in technologies for the development of unique credit assessment systems for a number of leading financial services organizations. Where there is an initiative to address new markets, or find good credit risks which demand expansion of traditional credit risk criteria, we have solutions. From data gathering, processing, storage and analysis. We can architect and develop systems that scale and integrate effectively.
DYNAMIC PRICING MODELS
Systems for the dynamic pricing and marketing of products and services can be tied to new types of data and the analysis of those data sets. These can run the gamut of practically anything where market factors can be optimized for performance.

Feedback loops can speed automation - where results of pricing strategies can be monitored, understood and acted upon with degrees of automation and supervised or unsupervised functionality.

Experience

for details on project case studies, get our Ebook CLICK HERE

WEB CRAWLING

Comprehensive internet information retrieval system for Equifax. Monitoring and maintenance

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CUSTOMER INTELLIGENCE: WEB

Design and preparation of a Customer Intelligence model: design of the event recovery model, lifecycle analysis, acquisition, activation, retention and churn.

Case Studies