Voice of the Industry

Using advanced machine learning for adaptive fraud prevention

Friday 13 March 2020 07:28 CET | Author Anda Kania | Voice of the industry

Fraud Prevention and Online Authentication Report 2019/2020

Rahul Pangam, the CEO of Simility, share his best practices on risk management strategies by presenting several powerful machine learning capabilities that can help businesses remain agile in their fight against fraud.

Fraud Prevention and Online Authentication Report 2019/2020

The rise of the digital-first economy has fundamentally shifted consumer behaviour, generated massive amounts of data, and created a thriving backdrop for fraud.

Customer expectations are higher than ever, forcing businesses to change how they interact with their customers while delivering a consistent and seamless experience across multiple channels. At the same time, advancements in technology have facilitated the ease of data collection, but have left businesses struggling with how to effectively manage and leverage their data.

As more and more data is placed online and the number of data breaches hits new milestones, it’s critical for businesses of all sizes to take a closer look at their fraud and risk management strategies.

Fighting fraud in a post-breach world

Endless data breaches continue to expose the personally identifiable information (PII) of millions of individuals. Armed with sensitive information and sophisticated tools, such as hidden networks, automated scripts, and out-of-the-box malware, fraudsters are well-equipped to launch a variety of complex attacks on organisations across the globe.

Constantly refining and adapting their tactics, fraudsters are relenless in their attempts to find and exploit vulnerabilities in a business’ ecosystem. Legacy solutions that rely on linear models, shared intelligence, or black box machine learning, are no longer sufficient to fight fraud in a post-breach world, and often lead to high false positives, friction, and operational inefficiencies.

As data, customer behaviour, and fraud continue to evolve, businesses need a flexible solution powered by advanced machine learning and robust link analysis to detect and block fraud in real time without negatively impacting the customer experience.

The power of machine learning

Leveraging vast amounts of data, machine learning algorithms can automatically detect patterns and make predictions to improve fraud detection and better protect businesses against losses. However, simply checking the box as to whether a platform is capable of machine learning is not enough. The dynamic environment in which businesses operate requires fraud prevention solutions that constantly adapt – and do so quickly. A number of powerful machine learning capabilities can help businesses remain agile in their fight against fraud.

Supervised and unsupervised models

While supervised learning uses labelled datasets to predict known patterns of fraud, a platform that is also capable of unsupervised learning can uncover patterns not yet classified as such. In the ever-evolving threat landscape in which sophisticated cybercriminals are constantly refining their tactics to evade detection, uncovering unclassified attacks in their early stages can give businesses an added defense.

Bring your own model with multi-language support

Many businesses have their own data science teams that have developed machine learning models customised for their unique needs.

Fraud Prevention and Online Authentication Report 2019/2020

Flexible platforms that enable businesses to deploy their own models on the platform, regardless of the language and algorithm used, will help businesses leverage their own insight for improved decisioning.

AutoML

Machine learning models deteriorate over time as data changes. AutoML offers data scientists a fast and efficient way to train, tune, validate, and deploy models. By automating this time-consuming process, models are regularly optimised to stay ahead of evolving fraud, and data scientists can scale their efforts.

Champion challenger

Routinely testing and comparing the efficacy of machine learning models ensures that the most accurate model is used for fraud decisioning. Platforms that allow multiple challengers to run on live data against the champion enable analysts to know the impact of model changes prior to implementing them. An analysis of results compares the performance of the models and an outperforming challenger can then be quickly deployed as the new champion in the live environment.

Explainability

Understanding why decisions are made by machine learning models is critical to help analysts investigate and audit high-risk cases. White-box machine learning provides transparency into risk scoring so that businesses have more control in the decisioning process and can make decisions based on their unique needs and risk tolerance.

Rules engine

Machine learning models can help businesses better manage their rules by automatically optimising existing rule performance, recommending new rules, testing rules, comparing rule performance, and simplifying rules. These capabilities help businesses maintain a high level of fraud detection accuracy and give businesses more agility to proactively fight fraud.

Learning to adapt

As fraudsters continue to take advantage of newly breached data and leverage their own sophisticated toolkit, including machine learning, businesses need to ensure that their fraud and risk strategy is not dependent upon legacy solutions. Businesses that are able to apply advanced machine learning for an adaptive approach to fraud prevention will be better able to successfully detect evolving fraud threats, enhance customer experience, improve operational efficiency, and address regulatory requirements.

Simility’s industry recognised fraud prevention platform employs advanced machine learning capabilities and is built with a data-first approach. It combines large volumes of data from multiple sources to provide a holistic view of the end user. The Adaptive Decisioning Platform gives businesses more insight, control, and flexibility to help uncover complex fraud patterns and adapt over time as fraud evolves and business needs change.

About Rahul Pangam

Rahul Pangam is the Co-Founder and CEO of Simility. He’s an industry veteran, who is dedicated to empowering fraud fighters with the most adaptable, scalable, and accurate fraud analytics platform.



About Simility

Fraud Prevention and Online Authentication Report 2019/2020

Simility offers real-time risk and fraud decisioning solutions to protect global businesses. Simility’s offerings are underpinned by the Adaptive Decisioning Platform, which is built with a data-first approach to deliver continuous risk assurance. By combining artificial intelligence and big-data analytics, Simility helps businesses seamlessly orchestrate decisions to reduce friction, improve trust, and solve complex fraud problems.


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Keywords: Simility, machine learning, risk management, fraud prevention
Categories: Securing Transactions | Digital Identity, Security & Online Fraud
Countries: World
This article is part of category

Securing Transactions