THE SCIENCE THAT’S TRANSFORMING THE WORLDDemystifying “cognitive technologies” from AI to machine learning – and exploring their growing role in compliance

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What are Cognitive Technologies?

Cognitive technologies take the key advantage of computing – the ability to process vast amounts of data at great speed – and then apply advanced algorithms and techniques to interpret data in new ways. In effect, they understand it in a manner that mimics how the human brain works. IBM summarizes cognitive technologies as: “radically disruptive systems that understand unstructured data, reason to form hypotheses, learn from experience and interact with humans naturally”.

The demand for these technologies is driven by the fact that the world is now saturated with data, much of which is potentially valuable. 90% of today’s data has been generated in the last two years1 as more and more human activity is captured in the digital realm. An internet-connected automobile, for example, transmits gigabytes of data every hour – about performance, road conditions, or wear and tear on components. Insights from this data could have huge value for everyone from manufacturers and roadbuilders to insurers and traffic authorities.

For another example, consider ‘predictive maintenance’ in industry. If a machine’s sensors are connected to a live store of performance data, a cognitive technology can enable engineers to identify potential problems before they occur, or schedule the optimum times for maintenance. Either way, this reduces downtime costs and boosts efficiency. And as the store of data builds, the system ‘learns’, becoming more efficient and valuable over time.

INTRODUCTION

You can’t go far in the business world today without encountering buzz about data and “cognitive technologies” such as machine learning and artificial intelligence. Yet despite the hype, it’s not always easy to find a ready, layperson’s explanation of what these technologies are and why they are so significant in so many areas of business. In this short whitepaper, we aim to demystify cognitive technologies, illustrate where and how they can be applied, and then finish by zooming in on how we plan to use them in our specialist discipline of compliance solutions.

of today’s data has been generated in the last two years.

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DEFINITIONS

1. Software robotics

The origin of the term ‘robot’ is the Czech word for forced labor, so it is appropriate that in cognitive technology, robotics refers to the automation of tedious, repetitive tasks. It is the simplest level of these technologies. In software robotics, programs carry out back-office tasks such as invoice processing, data management or HR administration. They can dramatically cut processing times compared to human operators, reduce errors and cut administration costs. When AI is applied to software robotics, it becomes able to improve and refine the way it works.

2. Data science

This is the overall discipline of extracting knowledge and insight from data assets using the cognitive technologies described in this paper. Harvard describes it as “an emerging discipline that draws upon knowledge in statistical methodology and computer science to create impactful predictions and insights for a wide range of traditional scholarly fields.” 2

3. Artificial Intelligence (AI)

AI is the catch-all term for technology that can perform human-like tasks, learn from experience and adjust when it receives new inputs. Rather than simply automating manual tasks as software robotics does, AI adds intelligence and improves itself through learning algorithms. AI is already featured in a huge range of applications, from search engine and ecommerce recommendations to voice recognition and fraud detection.

4. Machine Learning (ML)

When computer systems can improve their own performance without being explicitly programmed or ‘told’ what to do, that’s machine learning. This field of AI describes the capability for algorithms to take in information – e.g. images, data or video – and then come to their own conclusions about what to do with it. Machine learning can detect patterns in vast volumes of data and interpret their meaning. Applications include autonomous vehicles, image analysis and regulatory compliance.

5. Neural Networks

These are the architecture on which AI is often based. Modeled on the way a human brain works, neural networks are made up of interconnected nodes that respond to external inputs and relay information between one another. Using probability and feedback loops, they allow machines or programs to learn from complex information.

6. Deep Learning

A subset of ML, Deep Learning analyzes data within a neural network at different layers, allowing software to understand highly complex data and gradually train itself to be more effective by learning as it works. A subset of ML, Deep Learning is a neural network with more than two hidden layers that performs automatic feature extraction without human intervention. Deep Learning enables computers to construct meaningful features of the data to further learn, generalize and understand complicated concepts. It was a Deep Learning system that defeated a human player at the complex board game Go; it is also used in speech recognition and natural language processing.

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USE CASES

These AI and machine learning use cases illustrate how the technology can spot patterns and significant items among large and complex datasets.

Fraud detectionThis offers a simple illustration of the power of AI. A traditional fraud-detection program analyzes structured data against rules, for example flagging a transaction for investigation if it exceeds a certain threshold. This inevitably creates false positives that require time-consuming human review. An AI system, however, learns from the human decisions, gradually reducing the number of false positives and speeding up the fraud detection process.

Cognitive securityIBM estimates that the average security operations center deals with 200,000 items of security event data every day. If these alerts lack context, each must be dealt with equally, leading to a great deal of wasted time. AI can add that all-important context by picking up connections between data points that humans would miss, speeding up security response and making it more accurate.

Object recognitionIs it an iceberg or a ship? It’s obvious to a human observer, but could a satellite tell the difference from space? If it could, it would be able accurately map icebergs in real time and protect shipping. A competition on AI platform Kaggle is currently inviting developers to create such a system on behalf of energy company Statoil.

CybersecurityInformation on past incidents helps predict future threats. A US government entity has applied machine learning to a dataset of historical network threats and data to build models that can predict incoming threats on a continuous basis. Operators and analysts are able to identify potential threats and take action before they happen.

Personalizing medicationThe degree to which medication is effective can vary widely from individual to individual. IBM’s Watson machine learning platform is being used in the US to analyze tumor DNA against millions of pages of medical records to identify the most appropriate therapies for a specific patient. The result is a personalized treatment plan tailored to the patient.

Finding the Higgs bosonMachine learning played a part in the discovery at the Large Hadron Collider (LHC) of the Higgs boson, a particle proposed by the Standard Model in particle physics. Using simulations of the debris from particle collisions, algorithms at the experiments were trained to spot the patterns that a decaying Higgs particle would produce before being applied to real particle collisions.

items of security event data dealt with every day by the average security operations center

200,000

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

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SOFTWARE ROBOTICS

If a process is repetitive, chances are it can be automated. These examples show how software robotics is transforming back-office functions.

Mortgage processingA great deal of manual effort is involved in mortgage processing: ordering documents, entering and verifying data. Robotic process automation can dramatically reduce manual processing. Flood certificates, for example, can be ordered and read automatically, so human analysts can concentrate on due diligence for the small percentage of flood-zone loans. Addresses can also be verified automatically against official records, eliminating another repetitive task and saving valuable time.

HR process automationAn EY paper3 suggests that 93% of an HR employee’s time is spent on repetitive tasks and that 65% of rules-based processes in HR can be automated. Software robots can work alongside HR employees, doing repetitive and data-related tasks such as running reports, filling forms and submitting emails. EY suggests that processing times can be cut from hours to minutes and costs cut by over 50% on average.

Accounts payableProcessing invoices manually can be an error-prone process, especially when incomplete invoices have to be matched against the filing system. A KPMG robotic system automated the exception handling process, mapping information against an ERP system and correcting or populating fields as needed. The consultancy estimated its client increased productivity by up to 70 percent and made significant outsourcing cost savings.

50%REDUCTIONPROCESS AUTOMATION IN COSTS WITH HR

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WHICH TECHNOLOGY SOLVES WHICH PROBLEM?The cognitive technology you choose depends on the nature of the problem you need to solve. To make relatively simple back-office tasks more efficient, software robotics with a small degree of AI can make a huge difference, as the use cases have shown.

However, as problems become more conceptual, e.g. “Find transactions that are incompatible with anti-money laundering legislation”, the technology needs more of an ability to spot patterns, learn and suggest.

Data science and ML platform Kaggle suggests a set of questions to help identify the optimum technology:4

• What problem does it solve, and for whom?• How is it being solved today?• How can it beneficially affect business?• What are the data inputs and where do they come from?• What are the outputs and how are they consumed

(online algorithm, a static report, etc)• Is this a revenue leakage (“saves us money”) or

a revenue growth (“makes us money”) problem?

What does a data scientist’s profile look like? While there’s no official accreditation for the domain, the people needed to work in this field will typically have a similar set of qualifications, experience and attributes:

• Degree-level education – often to Master or PhD level – and strong quantitative skills

• Experience of programming languages (especially R and Python), good coding and database design skills

• Familiarity with disciplines such as statistical analysis, predictive modelling and hypothesis testing

• Insatiable curiosity

Platforms, Toolkits and Software Libraries Open-source tools and libraries are available to develop both AI and ML solutions:

• Keras – high-level neural networks API• Theano – Python library for defining, optimizing,

and evaluating mathematical expressions involving multi-dimensional arrays efficiently

• OpenNN –class library written in C++ which implements neural networks

• Caffe – deep-learning framework developed by Berkeley AI Research

• Tensorflow - software library for numerical computation using data flow graphs

• H20.ai – machine learning platform for big data analysis

SKILL SETS FOR DATA SCIENTISTS

350BILLION SCREENS

Next-generation screening platforms will:• Fundamentally change the economic model of

compliance screening• Dramatically reduce the false-positive and

excessive alerts that plague compliance operations• Enable teams to shift from reactive to proactive

working • Deliver greater productivity by moving to

a “machine-led, data-fueled” model• Improve screening efficiency through algorithms

that are constantly learning, improving and self-updating

in the last five years alone have given RDC algorithms unmatched prediction capability

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CONCLUSION: MORE EFFICIENCY, JUST WHEN IT’S NEEDEDReferences

1. Cognitive Technology: What It Is And Why Marketers Should Care, Forbes, June 2016

2. The Harvard Data Science Initiative

3. Robotic Process Automation for HR & Payroll, EY, 2016

4. Kaggle.com

For banks and fintechs, cognitive technologies look set to usher in a step-change in compliance and due diligence processes. It’s a promise that has arrived at the perfect moment. Growing regulation and fast-developing competition mean firms face a mounting administration burden at precisely the time when they need to be more agile and innovative to stay competitive. Ever-increasing customer demands, particularly in P2P lending and payments, are driving the need for faster response times.

With AI and machine learning reducing false positives, freeing up overworked teams to look for opportunities rather than firefight and creating more efficient screening processes, financial firms will be able to achieve both compliance and competitiveness. When markets are volatile and margins tight, the cognitive revolution is just what the industry needs.

DataThe backbone of any successful machine learning application is the data that feeds it. Our data assets include:- Our GRID database of 9M+ profiles of global entities including those on any of thousands of sanctions and other

watch lists, PEPs, and from global adverse media, with over 10,000 daily adds/updates- Client configuration settings, based on various factors such as risk profile, line of business, geography, etc.

providing templates for clients to leverage- Client inquiry data with each client’s data segregated and secured as required in a multi-tenant solution- Analyst and client decisions based on various client configurations, providing years of decisions and learnings

The RDC solution combines this vast data asset with technology, analytics and insight to substantially reduce false positives and provide material ROI.

Technology, Analytics and InsightRisk-based ProcessingWith this capability you can segregate your portfolio according to risk classification. Some clients use the classic high, medium and low rankings, while other break their customers down according to business line or geography.

ConfigurabilityRDC’s filtering and prioritization engines can be configured to match your risk tolerances through our CVIP process.

Constant LearningRDC’s algorithms were built from the overwhelming strength of our historical processing data. In the last five years alone, 350 billion screens have set the foundation for unmatched prediction capability. The data is segregat-ed by risk class, type, stage and several other material variables. Our processing engine constantly learns from its interaction with our clients individually and collectively, tracking metrics against these historical norms,highlighting outliers and new peer trends. Aside from predictability, the sheer size of this data facilitates peer-to-peer comparisons on several fronts: (1) filtering; (2) prioritization; (3) false positive remediation; and (4) results tailoring.

Instantaneous, probabilistic outcome-based scoringAt RDC, our goal is to keep compliance highly functional, easy to use and cost-effective. With a real-time synchronous API that can be exposed on various ecosystems, sanctions screening becomes a seamless part of the KYC process that is automatically scalable.

RDC SOLUTIONS

Image of Large Hadron Collider supplied by CERN

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Discover more rdc.comCopyright RDC, Authored by Michael Aguiling, CTO

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