Powering Next-Generation Fraud Prevention and Security Intelligence Through Advanced Analytics and AI

Harnessing cutting edge technologies to protect data, prevent fraud, improve customers experiences, safeguard reputations and strengthen compliance

In the digital age, the implications of financial crime against banks and other financial services institutions are accelerating rapidly. Fraud prevention now represents one of the biggest areas of concern for the financial services industry and is likely to have bigger ramifications in the coming years.

The loss of revenue due to frauds are staggering - but nothing compared to the real costs in lost productivity and damage to reputations and customer confidence. Then there is the fraud that goes undetected, impossible to account for, and assess.

The ramifications are far-reaching, and regulators demand that financial institutions hold more capital and get more proactive about tackling fraud and fraudulent activities.

The current global pandemic of Covid-19 is making the matters worse. While economies decline and employment rates drop drastically, cases of fraud are known to rise during recessions.

Global cases of financial scams and cyber/online fraud in the banking sector are increasing as criminals are finding new ways to steal from banks and their customers.

The shift to working from home during the COVID-19 pandemic has led to an increase in mobile phishing campaigns, with attackers targeting remote workers whose devices lack adequate security protections. Threat actors are exploiting people’s fear and financial weakness resulting from the pandemic.

Many of these campaigns are designed to steal users' banking credentials.

Fraudsters are becoming more sophisticated today and are leveraging technology to scam more victims. This means financial institutions need a paradigm shift in their approach to mitigate fraud risks sustainably and effectively.

To meet increasing customer expectations, financial institutions need to focus on building a well-structured fraud management model that can deal with evolving digital transformation, identify unknown risks, harness the benefits of technology and reduce the cost of compliance.

Proactive Fraud Protection Through Advanced Analytics, AI and Machine Learning

Artificial intelligence (AI) has the potential to transform financial institutions (FIs), disrupting every aspect of financial services, from the customer experience to financial crime.

One of the most compelling use cases for AI is in the battle against financial crime. AI has two primary benefits for the banks engaged in this battle: it can increase the effectiveness and efficiency of financial crime investigations, and the institutionalise risk management.

Financial institutions can employ AI to analyse large amounts of data, to filter out false alerts and identify complex criminal conduct. It can identify connections and patterns that are too complex to be picked up by straightforward, rule-based monitoring, or the human eye.

This points to four basic questions:

• Are financial institutions ready to embrace advances in ML to help uncover emerging patterns for preventing fraud
• How can financial institutions harness expanded data typologies generated by new authentication processes for better fraud detection?
• How can financial institutions manage the data orchestration challenges to leverage different data sources, integrate with other information, and factor in decision making across the entirety of the customer journey?
• Are financial institutions benefitting from better data orchestration?

Enhancing AML Monitoring and Driving Operational Efficiency

Digital transformation and artificial intelligence are undeniably changing the Anti-Money Laundering landscape. The need to modernise AML processes, coupled with a regulatory push towards innovation, is driving financial institutions to enhance AML monitoring & drive operational efficiency.

The manual and semi-automated nature of current AML compliance efforts slow down processing timelines and impact business productivity.

The immense volume of data that financial institutions are expected to comb through to meet regulatory requirements to detect and report suspicious activity becomes a daunting challenge.

The data is usually diverse and subpar. It’s common for systems to use only a subset of available data when generating alerts. Traditional transaction monitoring systems are unwieldy to maintain and rely on rules and thresholds that are easy for criminals to test and circumvent.

Investigation processes tend to be highly manual, from gathering the supporting data for a case to submitting a complete SAR (suspicious activity report). Meanwhile, the money launderers are working night and day to remain hidden, constantly engineering new ways to conceal the flow of funds.

Traditional anti-money laundering (AML) and combating the financing of terrorism (CFT) tools and tactics take longer and cost more than they should.

To fortify the defence, financial institutions need ways to:

• Automate tasks that formerly required human intervention, such as disposition of alerts
• Detect more risk and effectively prioritise it with sophisticated analytics techniques
• Provide richer context for investigations with access to more comprehensive insights

OpenGov is pleased to invite you to our exclusive OpenGovLive! Virtual Insight aimed at imparting knowledge on how financial institutions can apply real-world AI & analytics applications to ensure a world-class integrated banking system that contributes to improve customer experiences, enterprise profitability, manage risk and regulatory compliance, anticipate fraud and create value from data.

This session serves as a great peer-to-peer learning platform to gain insights and practical solutions to understand the value of cutting edge technologies available to make better, faster, and more cost-effective, data-driven decisions.

We will be discussing:

• Harnessing advanced analytics and AI technologies to enable a proactive, robust and unified strategy for enterprise-wide fraud and security intelligence
• Delivering high-performance analytics and multiple detection methods to monitor more risks in very large data volumes
• Integrating automation, AI and machine learning into financial crime programs and leveraging potential efficiencies
• Transaction monitoring for suspicious activity and reducing false positives using technology advances
• Developing new techniques for sound compliance practices for anti-bribery and corruption laws across jurisdictions
• Aligning fraud and cyber teams to increase cyber resiliency and minimise risk
• Strategies to break down data silos, adjust to shifting regulations, and safeguard against present and future risks
• Utilising innovative technologies to protect data, customers, reputations, and compliance
• Discussing intuitive supervised and unsupervised machine learning-based models to offer out of box processes
• Standardising and managing strategic and operational risks and consolidate information into an enterprise-wide view of risk to strengthen governance and foster trust

Who should attend:

• Chief Legal Officers
• Chief Compliance Officers
• Heads of Fraud and AML
• Chief Risk Officers
• Head of KYC / Monitoring & Screening
• Head of Fraud Prevention and Analytics
• Head of Investigation
• Head of Customer Experience – Fraud & Security
• Heads of Cybersecurity