Problems / Use Cases

How GRC technology can overcome your compliance problems
AML CFT

We automate the processing of gathering business data from multiple sources inside and outside the organization, regardless of the format or whether some of the data is in paper or voice form. This data is automatically ingested, enabling authorized personnel to find data very quickly.

Our decision support approach is to adapt relevant, contextual payments, transactional data (SWIFT, CHIPS, and FedWire etc.) into Atomic Data (smallest meaningful element of data for AML CFT scoring), enrich this data by expanding the Bank Identity Code, and extend this data through the bank’s data and our data-as-a-service, which gathers data from public sources to identify firm ownership, etc.

By applying multiple-rule programs, each containing a different rule set, our Atomic & Molecular Engine is able to apply a variety of scoring techniques for profiling the end-to-end risk. For example, the extended SWIFT MT103 payment instruction in the Atomic Data format covers all the relevant and contextual data for risk scoring, covering the Originating Firm, the Beneficiary Firm, their respective banks and up to three intermediary banks. This may involve up to seven countries obtained directly from the SWIFT MT103, plus potentially other countries involving ownership structures.

In essence, this approach covers KYC (know your customer), KYCC (know your customer’s customer), EDD (enhanced due diligence), and UBO (ultimate beneficial owner). If critical missing data, such as a UBO, is not identified, then a risk score is applied – this is how we cover known unknowns.

Using controlled human recalibration our solution becomes progressively more intelligent over time, to keep pace with changing strategic priorities and changing business behaviors.

Benefits:

  • Better protection of the bank’s brand from ‘unknown risks’
  • Reduce balance sheet provisions from regulator fines and trade restrictions
  • Identify anomalies not traditionally identified by algorithmic systems
  • Materially reduce the costs of false positive alerts
  • Identify false negatives missed by the bank
  • Sense early and respond quickly to latent or emergent risks
  • Collapse the cost of coordinating the gathering of internal and external information
  • Develop deeper and deeper contextual rule sets
  • Progressively grow smarter and smarter machine intelligence
  • Real-time audit trail of machine decisions
  • Controlled calibration and recalibration of risk scoring sets
  • Audit trail of recalibrated changes
  • Better profiling to drive market and revenue opportunities, whilst reducing risks
  • Profiling adapts to the emergent transactional patterns
  • Collect, organize and review any type of data, including paper-to-data and voice-to-data
Business Client On-boarding

We bring new levels of automation and capabilities to business client on-boarding, including over multiple jurisdictions. First, we are able to collect customer data from multiple sources inside and outside the organization, regardless of the format or whether some of the data is in paper or voice form. This data is automatically ingested enabling authorized personnel to find data very quickly.

Our decision support approach is to adapt relevant, contextual customer data into Atomic Data (smallest meaningful element of data for customer scoring) and extend this data through our data-as-a-service, which gathers data from public sources. By applying multiple-rule programs, each containing a different rule set, our Atomic & Molecular Engine is able to apply a variety of scoring techniques for profiling the customer, including suitability and risk profiling, appetite and capability of the prospect to undertake risk and their propensity to buy, etc.

Using controlled human re calibration our solution becomes progressively more intelligent over time, to keep pace with changing strategic priorities and changing customer behaviors.

Benefits:

  • Reduce the client on-boarding costs, elapsed time and risks
  • Collect, organize and review any type of data, including paper-to-data and voice-to-data
  • Collapse the cost of coordinating the gathering of information
  • Configure different customer scoring techniques
  • Improve understanding of complex customer patterns
  • Identify anomalies not traditionally identified by algorithmic systems
  • Develop deeper and deeper contextual rule sets
  • Progressively grow smarter and smarter machine intelligence
  • Real-time audit trail of machine decisions
  • Controlled calibration and recalibration of risk scoring sets
  • Audit trail of recalibrated changes
Banking Governance Risk & Compliance

Risk management in banking has been transformed over the past decade, largely in response to regulations that emerged from the global financial crisis and the fines levied in its wake. But important trends are afoot that suggest risk management will experience even more sweeping change in the next decade.

The change expected in the risk function’s operating model illustrates the magnitude of what lies ahead. Today, about 50 percent of the function’s staff are dedicated to risk-related operational processes such as credit administration, while 15 percent work in analytics. Research suggests that by 2025, these numbers will be closer to 25 and 40 percent, respectively.

No one can draw a blueprint of what a bank’s risk function will look like in 2025—or predict all forthcoming disruptions, be they technological advances, macroeconomic shocks, or banking scandals. But the fundamental trends do permit a broad sketch of what will be required of the risk function of the future. The trends furthermore suggest that banks can take some initiatives now to deliver short-term results while preparing for the coming changes. By acting now, banks will help risk functions avoid being overwhelmed by the new demands.

GRC involves cooperation among a diverse group of stakeholders to share a variety of sensitive data. Significant time, energy and money are spent on bringing the disparate data sources together for analysis. The modus operandi is often based towards statistical analysis, though such techniques are less effective for the ‘long tail’, which is the portion of data having a large number of occurrences far from the ‘head’ or central part of the distribution. Yet it is in the long tail that often material threats or opportunities occur.

We automate the processing of gathering relevant data from multiple sources inside and outside the organizations involved with the risk monitoring, regardless of the format or whether some of the data is in paper or voice form. This data is automatically ingested, enabling authorized researchers to find data very quickly.

Our decision support approach is to adapt the relevant information into Atomic Data (smallest meaningful element of data for scoring) and extend this data through our data-as-a-service, which gathers data from public sources. By applying simultaneously multiple-rule programs, each containing a different rule set, our Atomic & Molecular Engine is able to detect anomalies often missed by conventional algorithmic systems.

Using controlled human recalibration our solution becomes progressively more intelligent over time, to keep pace with changing behaviors of fraudsters.

Benefits:

  • Deliver Business Insights: Through the use of business data (structured and unstructured) to produce insights which allow us to improve the management of performance and their compliance.
  • Key Risk Indicators: Identification and implementation of indicators which will trigger when advisor behaviours are starting to move outside of a pre-defined business risk appetite.
  • Controls Testing: Automation of key control testing to provide the business with more timely information on control failures through near real-time testing and to expand the control testing across the entire dataset.
  • Improve the ability for teams to understand and better manage risks relating to advisors based on data analytics.
  • Faster to market – business insights
  • Timely identification of policy breaches
  • Opportunity to provide capability uplift
  • Real-time performance management
  • Increased regulatory confidence
  • Delivers a self-service user-defined tool
  • Non-static reporting
  • Tackle the long tail for identifying threats and opportunities
  • Collapse the cost of coordinating the gathering of information
  • Gather evidence faster and better to support intervention
  • Collect, organize and review any type of data, including paper-to-data
  • Accelerate the time to research by following dynamic pathways
  • Develop deeper and deeper contextual understanding
  • Real-time audit trail of changes
  • Improve understanding of complex patterns through customized iconography
  • Freeze evidence so it cannot be tampered with
  • Parallel users contributing with restrictive access
  • Progressively grow smarter and smarter machine intelligence
  • Real-time audit trail of machine decisions
  • Controlled calibration and recalibration of risk-scoring sets
  • Audit trail of recalibrated changes
Crime and Forensic Case Management

We automate the processing of gathering forensic data from multiple sources inside and outside the organization, regardless of the format or whether some of the data is in paper or voice form. This data is automatically ingested, enabling investigators to find and understand data very quickly.

Our Forensic Case Management provides a flexible solution for collaborative investigations. Users manage, investigate and track cases as well as add new information through simple data entry forms, with the ability to add related documents.

With advanced visualization integrated into the forensic case management, it provides a universal and unified means to undertake forensic case management.

Benefits:

  • Materially reduce the elapsed time for investigations
  • Gather evidence faster and better to support successful prosecutions
  • Collect, organize and review any type of data, including paper-to-data
  • Collapse the cost of coordinating the gathering of information
  • Accelerate the time to investigate by following dynamic pathways
  • Develop deeper and deeper contextual understanding
  • Real-time audit trail of changes
  • Improve understanding of complex patterns through customized iconography
  • Freeze evidence so it cannot be tampered with
  • Parallel investigators contributing with restrictive access
  • Progressively grow smarter and smarter machine intelligence
  • Real-time audit trail of machine decisions
  • Controlled calibration and recalibration of risk scoring sets
  • Audit trail of recalibrated changes
Fraudulent Insurance Claims

We automate the processing of uncovering fraudulent insurance claims and red flags, in real time, by identifying anomalies that require further investigation. Our decision support approach is to adapt the claim information into Atomic Data (smallest meaningful element of data for scoring) and extend this data through our data-as-a-service, which gathers data from public sources. By applying simultaneously multiple-rule programs, each containing a different rule set, our Atomic & Molecular Engine is able to detect anomalies often missed by conventional algorithmic systems.

Using controlled human recalibration our solution becomes progressively more intelligent over time, to keep pace with changing behaviours of fraudsters.

Benefits:

  • Improve the margins of the business
  • Reduce the costs of claims
  • Collapse the cost of coordinating the gathering of internal and external information
  • Sense early and respond quickly to changes
  • Identify anomalies not traditionally identified by algorithmic systems
  • Develop deeper and deeper contextual rule sets
  • Progressively grow smarter and smarter machine intelligence
  • Real-time audit trail of machine decisions
  • Controlled calibration and recalibration of risk scoring sets
  • Audit trail of recalibrated changes
Healthcare Data

Healthcare providers need to handle greater volumes, variety and velocity of data to support machine and human decisions. There is a growing realization that data needs to be collected and adapted across multiple siloed systems and the growing diversity of consumer data driven by mobile health apps, wearable health technologies and other forms of sensor data needs to be embraced.

We automate the processing of gathering relevant data from multiple sources inside and outside the organization, regardless of the format or whether some of the data is in paper or voice form. This data is automatically ingested, enabling authorized personnel to find data very quickly.

Our decision support approach is to adapt relevant, contextual consumer (includes patient) health data into Atomic Data (smallest meaningful element of data for healthcare scoring) and extend this data through our data-as-a-service, which gathers data from public sources. By applying multiple-rule programs, each containing a different rule set, our Atomic & Molecular Engine is able to apply a variety of scoring techniques for profiling healthcare data, in context with supporting a diversity of decisions.

Using controlled human recalibration our solution becomes progressively more intelligent over time, to keep pace with changing strategic priorities and changing business behaviors.

Benefits:

  • Improve the support for evidence-based decisions
  • Reduce the risks of poor decisions
  • Collapse the cost of coordinating the gathering of internal and external information
  • Sense early and respond quickly to changes
  • Identify anomalies not traditionally identified by algorithmic systems
  • Develop deeper and deeper contextual rule sets
  • Progressively grow smarter and smarter machine intelligence
  • Real-time audit trail of machine decisions
  • Controlled calibration and recalibration of risk scoring sets
  • Audit trail of recalibrated changes
Health Insurance Analytics

Health insurance companies seek to control fraud perpetrated by healthcare providers, pharmacists and patients, and manage investigations from detection to prosecution. This often involves significant data challenges which lead to suboptimal productivity and outcomes.

We automate the processing of gathering relevant data from multiple sources inside and outside the organization, regardless of the format or whether some of the data is in paper or voice form. This data is automatically ingested, enabling authorized investigators to find data very quickly.

Our decision support approach is to adapt the relevant information into Atomic Data (smallest meaningful element of data for scoring) and extend this data through our data-as-a-service, which gathers data from public sources. By applying simultaneously multiple-rule programs, each containing a different rule set, our Atomic & Molecular Engine is able to detect anomalies often missed by conventional algorithmic systems.

Using controlled human recalibration our solution becomes progressively more intelligent over time, to keep pace with changing behaviours of fraudsters.

Benefits:

Materially reduce the elapsed time for investigations

Gather evidence faster and better to support successful prosecutions

Collect, organize and review any type of data, including paper-to-data

Collapse the cost of coordinating the gathering of information

Accelerate the time to investigate by following dynamic pathways

Develop deeper and deeper contextual understanding

Real-time audit trail of changes

Improve understanding of complex patterns through customized iconography

Freeze evidence so it cannot be tampered with

Parallel investigators contributing with restrictive access

Progressively grow smarter and smarter machine intelligence

Real-time audit trail of machine decisions

Controlled calibration and recalibration of risk scoring sets

Audit trail of recalibrated changes

Insider Threat

Organizations in the public and private sectors involve thousands of employees interacting with data that can be political, proprietary, sensitive, influential, problematic and monetary, or other forms of intrinsic value. The costs and damage associated with losing, misusing, or abusing this data make insider threats one of the most dangerous risks facing enterprises today. By sensing early and responding quickly to latent or emergent threats requires new capabilities that can cope with the volume, variety and volatility of the relevant and contextual nature of the data.

We automate the processing of gathering relevant data from multiple sources inside and outside the organization, regardless of the format or whether some of the data is in paper or voice form. This data is automatically ingested, enabling authorized investigators to find data very quickly.

Our decision support approach is to adapt the relevant information into Atomic Data (smallest meaningful element of data for scoring) and extend this data through our data-as-a-service, which gathers data from public sources. By applying simultaneously multiple-rule programs, each containing a different rule set, our Atomic & Molecular Engine is able to detect anomalies often missed by conventional algorithmic systems.

Using controlled human recalibration our solution becomes progressively more intelligent over time, to keep pace with changing behaviors of fraudsters.

Benefits:

  • Better protection of the organization’s brand from ‘unknown risks’
  • Reduce balance sheet liabilities from unknown risks
  • Identify anomalies not traditionally identified by algorithmic systems
  • Materially reduce the costs of false positive alerts
  • Identify false negatives missed by conventional monitoring systems
  • Sense early and respond quickly to latent or emergent risks
  • Collapse the cost of coordinating the gathering of internal and external information
  • Develop deeper and deeper contextual rule sets
  • Progressively grow smarter and smarter machine intelligence
  • Real-time audit trail of machine decisions
  • Controlled calibration and recalibration of risk scoring sets
  • Audit trail of recalibrated changes
  • Collect, organize and review any type of data, including paper-to-data, voice-to-data
Know Your Customer

We automate the processing of gathering customer data from multiple sources inside and outside the organization, regardless of the format or whether some of the data is in paper or voice form. This data is automatically ingested, enabling authorized personnel to find data very quickly.

Our decision support approach is to adapt relevant, contextual customer data into Atomic Data (smallest meaningful element of data for customer scoring) and extend this data through our data-as-a-service, which gathers data from public sources. By applying multiple-rule programs, each containing a different rule set, our Atomic & Molecular Engine is able to apply a variety of scoring techniques for profiling the customer, including the propensity to buy, identifying new behavioral patterns, and highlighting increased risks, etc.

Using controlled human recalibration our solution becomes progressively more intelligent over time, to keep pace with changing strategic priorities and changing customer behaviors.

Benefits:

Collect, organize and review any type of data, including paper-to-data and voice-to-data

Collapse the cost of coordinating the gathering of information

Sense early and respond quickly to changes

Improve understanding of complex customer patterns

Identify anomalies not traditionally identified by algorithmic systems

Develop deeper and deeper contextual rule sets

Progressively grow smarter and smarter machine intelligence

Real-time audit trail of machine decisions

Controlled calibration and recalibration of risk scoring sets

Audit trail of recalibrated changes

Mergers & Acquisitions

The sheer volume of data within the average data room makes it very difficult to assess and analyze manually. To make matters worse, the average data room is a minefield of inconsistent structured and unstructured data, requiring analysis by professionals.

The new generation is the creation of a ‘Living Data Room’ that accesses all relevant, contextual data inside and outside of the organization. We automate the processing of holistic data from multiple sources inside and outside of the organization in context with supporting decisions for acquisitions.

Our decision support approach is to adapt relevant, contextual targeted data into Atomic Data (smallest meaningful element of data for customer scoring) and extend this data through our data-as-a-service, which gathers data from public sources. By applying multiple-rule programs, each containing a different rule set, our Atomic & Molecular Engine is able to apply a variety of scoring techniques for profiling the target company from multiple perspectives: market, finance, sales, operations, customers, social and other dimensions that are relevant to the acquirer.

Using controlled human recalibration our solution becomes progressively more intelligent over time, enabling the acquirer and its advisors to handle increasing levels of complexity within decreasing time periods.

Benefits:

  • Avoid buying inappropriate companies
  • Provide better assurance to protect the brand from poor purchasing risks
  • Collect, organize and review any type of data, including paper-to-data and voice-to-data
  • Collapse the cost of coordinating the gathering of information
  • Sense early and respond quickly to changes
  • Improve understanding of complex prospect patterns
  • Identify anomalies not traditionally identified by algorithmic and human systems
  • Develop deeper and deeper contextual rule sets
  • Progressively grow smarter and smarter machine intelligence
  • Real-time audit trail of machine decisions
  • Controlled calibration and recalibration of risk scoring sets
  • Audit trail of recalibrated changes
Patent Lifecycle Management

The sheer volume of data within the average data room makes it very difficult to assess and analyze manually. To make matters worse, the average data room is a minefield of inconsistent structured and unstructured data, requiring analysis by professionals.

The new generation is the creation of a ‘Living Data Room’ that accesses all relevant, contextual data inside and outside of the organization. We automate the processing of holistic data from multiple sources inside and outside of the organization in context with supporting decisions for acquisitions.

Our decision support approach is to adapt relevant, contextual targeted data into Atomic Data (smallest meaningful element of data for customer scoring) and extend this data through our data-as-a-service, which gathers data from public sources. By applying multiple-rule programs, each containing a different rule set, our Atomic & Molecular Engine is able to apply a variety of scoring techniques for profiling the target company from multiple perspectives: market, finance, sales, operations, customers, social and other dimensions that are relevant to the acquirer.

Using controlled human recalibration our solution becomes progressively more intelligent over time, enabling the acquirer and its advisors to handle increasing levels of complexity within decreasing time periods.

Benefits:

  • Avoid buying inappropriate companies
  • Provide better assurance to protect the brand from poor purchasing risks
  • Collect, organize and review any type of data, including paper-to-data and voice-to-data
  • Collapse the cost of coordinating the gathering of information
  • Sense early and respond quickly to changes
  • Improve understanding of complex prospect patterns
  • Identify anomalies not traditionally identified by algorithmic and human systems
  • Develop deeper and deeper contextual rule sets
  • Progressively grow smarter and smarter machine intelligence
  • Real-time audit trail of machine decisions
  • Controlled calibration and recalibration of risk scoring sets
  • Audit trail of recalibrated changes
Prevention-Avoid Misselling Financial Products

We automate the processing of gathering prospective customer data from multiple sources inside and outside the organization, in context with supporting decisions for selling investment, banking, and insurance products.

Our decision support approach is to adapt relevant, contextual prospective customer data into Atomic Data (smallest meaningful element of data for customer scoring) and extend this data through our data-as-a-service, which gathers data from public sources. By applying multiple-rule programs, each containing a different rule set, our Atomic & Molecular Engine is able to apply a variety of scoring techniques for profiling the customer, such as avoiding fraudulent attempts, suitability to buy, and identifying cross-sell and up-sell opportunities.

Using controlled human recalibration our solution becomes progressively more intelligent over time, to keep pace with changing strategic priorities and changing prospective customer behaviors.

Benefits:

  • Avoid selling to inappropriate individuals and companies
  • Provide better assurance to protect the brand from misselling risks
  • Collect, organize and review any type of data, including paper-to-data and voice-to-data
  • Collapse the cost of coordinating the gathering of information
  • Sense early and respond quickly to changes
  • Improve understanding of complex prospective customer patterns
  • Identify anomalies not traditionally identified by algorithmic systems
  • Develop deeper and deeper contextual rule sets
  • Progressively grow smarter and smarter machine intelligence
  • Real-time audit trail of machine decisions
  • Controlled calibration and recalibration of risk scoring sets
  • Audit trail of recalibrated changes
Custom Solutions

Our context-data broker capability is a service that is designed to gather reachable context data of a variety of types, sources and velocity. It then applies conditioning, integration, rules and analytics to derive the reduced prepared context data, actionable at a point of business decision by a system or a human. Using our Data Life Cycle we are able to free data for the reinvention, digitization or elimination of business processes.

Our approach for custom solutions is to use our platform to model the current and target processes, to define the desired measurable outcomes, identify the holistic data and prioritization for generating value.

We automate the processing of gathering prospective relevant and contextual data from multiple sources inside and outside the organization, in context with supporting decisions for selling investment, banking, and insurance products.

Our decision support approach is to adapt relevant, contextual prospective customer data into Atomic Data (smallest meaningful element of data for scoring) and extend this data through our data-as-a-service, which gathers data from public sources. By applying multiple-rule programs, each containing a different rule set, our Atomic & Molecular Engine is able to apply a variety of scoring techniques in content to the target processes and measurable outcomes.

Using controlled human recalibration our solution becomes progressively more intelligent over time, to keep pace with changing strategic priorities and changing prospective customer behaviors.

Benefits:

  • Collect, organize and review any type of data, including paper-to-data and voice-to-data
  • Collapse the cost of coordinating the gathering of information
  • Sense early and respond quickly to changes
  • Improve understanding of complex prospective patterns
  • Identify anomalies not traditionally identified by algorithmic systems
  • Develop deeper and deeper contextual rule sets
  • Progressively grow smarter and smarter machine intelligence
  • Real-time audit trail of machine decisions
  • Controlled calibration and recalibration of risk scoring sets
  • Audit trail of recalibrated changes