In the years since the financial crisis, there has been a significant focus within the Financial Services Sector on conservative, organic growth while managing risk and compliance expectations. In recent months, the Sector has been experiencing a shift. The rapid adoption of Fintech and Regtech, combined with the ever-increasing pressure on cost reduction, has created an opportunity for leading practice, established firms to adopt an information-led strategy, for rapid growth and market share, while effectively managing risk and compliance. Further opportunity arises as upstart Fintech’s stumble in their attempts to gain market share from the well-established banks and diversified financial services firms (firms).
Below, I highlight a few of the leading trends that are rapidly emerging in 2019.
Data to Support Insight-driven Banking
Over the past few years, most financial services firms have focussed on ensuring the quality of their data used for regulatory reporting purposes. As a result of this hard work and investment, these firms are well-positioned to monetize these data through the application of advanced and cognitive analytics. Firms are discovering that their risk and regulatory compliance reports rely on the same quality, governed data and customer master data required for top-line expansion.
Specific opportunities include enhancing:
· Customer retention programs, identifying customers likely to move part or all of their business to other firms.
· Cross and upselling, identifying current customers who are candidates for expanding their business through additional products and/or services.
· Predictive lending, identifying customers who may be ready to purchase or refinance a home.
As firms embark on this journey, fortifying their proprietary in-house data is a unique competitive advantage of the established players relative to the emerging Fintechs. As a result, we are seeing leading practice firms accelerating their journey to monetize their internal data through utilizing existing advanced cognitive analytics tools, such as IBM’s Customer Insights for Banking and other unique accelerators.
Data Privacy Programs
Stories on the front pages of the Wall Street Journal about data theft, data breaches and the improper uses of customer data are becoming all-too-frequent. Without trust in the institution and its ability to safeguard, manage, and use data appropriately – business is lost. In addition to these business drivers for data protection, the regulators are actively enforcing GDPR and California’s Privacy Law goes into effect on January 2020.
It is critical that organizations protect the privacy of its customer data and that information be managed to the highest of standards. To achieve this, leading practice financial services firms proactively address data privacy through:
· Development of data protection strategy
· Safeguards for sensitive data
· Continuous monitoring of data access
· Stringent privacy policies, to which the firm audits and adheres
To facilitate these leading practices, firms are utilizing services and tools to help ensure customer trust and regulatory compliance.
Payment Fraud Prevention through Cognitive Analytics
As the number of online consumer payment options propagate, so does the opportunity and incidence for payment fraud. Global criminal networks are increasing their adeptness in exploiting the weakness in law enforcement, regulatory oversight, and consumer naiveté about the complex payments landscape. Stolen credit card numbers are available for $5 each on the dark web. Entire buildings overseas house the manufacture of fake cards. Leaders of criminal networks operate from overseas, with only the occasional in-country “mule” getting caught.
Unfortunately, payment providers are largely left on their own, to not only devise methods for fraud prevention, but also to determine the nature of fraud in the first place. To meet these challenges, leading practice financial services organizations address their fraud problems proactively through:
· Cognitive, self-learning, self-correcting detection capabilities to facilitate rapid adaptation of detection and prevention algorithms for rapidly evolving criminal patterns and behavior.
· Intelligent insight into cross-channel data and patterns to detect complex fraud and shut down related accounts across those channels.
· Intelligence sharing amongst traditionally competing financial institutions as they realize that they can achieve net benefit by sharing information about criminal networks and fraud patterns not only through trading ideas at conferences, but also through utilities that provide near real-time intelligence.
Fortunately, there are suites of tools that facilitate financial services firms in adopting these leading practices. For example, IBM’s Safer Payments is an IBM platform that supports proactive, cognitive capabilities for payment fraud alerts.
Trade and Conduct Surveillance
Financial Services firms face major challenges in improving trade and conduct surveillance output and mitigating risk, thereby reducing losses, fines, and reputation risk. Threats posed by innovative bad actors inside major financial services firms are increasing at the global level. As a result, financial Services firms need to surveil and monitor trading activity and the communications between employees and customers taking place around that activity.
While traditional structured data may be relatively simple to query and analyse, communication data, on the other hand, comes in a variety of unstructured formats including voice, email, instant messages, even video files, therefore is much harder to unpack and analyse for relevance. Complicating matters is that the sources of the surveillance data required to identify the bad actors needs to be integrated from multiple sources. It is not simply transaction and communications data, but also Product Control, HR, AML, and KYC data that helps identify individuals of concern.
To meet these challenges, established financial services firms are embracing the integration of digital, data, analytics, and risk management into all facets of their business and operating model, applying a holistic cognitive approach to trade surveillance to detect, profile, and prioritize complex trading scenarios. These approaches leverage advanced and cognitive analytics to learn from post-event investigations and consider external events into risk factor analysis.
Using cognitive analytics, a unified surveillance system builds out risk scenarios based on multiple risk factors and exposures to those risk factors to identify deviations from normal patterns of behavior, as they occur. For example, a breach in a trader’s limits correlated to a decision not to take annual leave might become of greater interest to supervisors.
By utilizing a cognitive system which can both "remember" and effectively "reason" through a known violation type, compliance officers can achieve greater efficiency by reviewing low-level alerts in aggregation and empower better decisions with the use of more data in context. To facilitate these leading practices, there are a number of leading practice tools, such as IBM’s Surveillance Insight for Financial Services.
Digital Audit, Compliance, and Risk Monitoring and Testing
To manage the ever-increasing risk and compliance challenges faced by today’s financial services firms, there has been a proliferation of new audit and regulatory compliance tests and risk monitoring requirements. As a result, there is a proliferation of manual processes, inconsistent access to data, partially documented controls environment, and a lagging reaction to new and/or changing regulations. The traditional approach to address this increased workload has been to hire additional staff – taking on exponentially higher costs.
Leading practice financial services firms are utilizing enhanced digital capabilities to streamline the audit, compliance testing, and surveillance environments, across all three lines of defence. These firms are implementing:
· Automated workflow tools for the development and implementation of enhanced controls and testing
· Robotic process automation tools to automate and accelerate rote testing activities
· Advanced and cognitive analytics to automate complex human reasoning, that is consistent and doesn’t get tired
The utilization of these digital tools enables firms to move from sample testing to full population review. This results in decision-makers having access to real-time information that provides deeper insights for timely decision-making.
Anti-Money Laundering and Know your Client
New and increasingly similar regulations across jurisdictions have rapidly recast AML from a standalone compliance function to a progressively complex, overarching operation affecting or incorporating legal, risk, compliance, operations, and tax departments. To meet these challenges, proactive financial services organizations are:
· Integrating cross-organization data, from a traditional siloed approach by combining insights from multiple detection systems, including KYC, suspicious activity monitoring, and watchlist filtering.
· Using advanced and cognitive analytics, to transform risk and compliance management and improving decisioning, from regulatory change management to specific compliance processes. Regulators are beginning to expect that institutions incorporate improved technology in their AML programs.
· Adopting a risk-based approach, to modernizing transaction monitoring systems, to reduce the operational overload from legacy systems with high false positive rates.
· Implementing back-office process automation, to drive operational efficiency and refocus resources from mundane tasks.
To facilitate these leading practices, it is recommended that traditional AML/KYC compliance workflow tools be integrated with other leading practice tools, such as IBM’s Alerts Insight with Watson.
Why Addressing These Trends is Important?
Depending on the firm’s current position, there are two primary reasons to address these trends in 2019:
· Top-line expansion: Many traditional firms have addressed the underlying regulatory and risk issues embedded within these trends. Therefore, the highest return payoff is monetizing the investments made in regulatory compliance and risk. There is huge potential top-line expansion waiting to be tapped in firms’ existing data.
· Increased regulatory scrutiny: Firms that have not addressed the fundamentals of risk management and regulatory compliance will be under increased scrutiny, by both US and foreign regulators, as well as customers. This heightened risk lens is becoming more important as Fintechs experience missteps with customers and markets and interest rates continue to their volatility.
In future blogs this year, I will further detail these trends, providing specific techniques and examples of remediating these risks while using these solutions for top-line expansion opportunities.
For additional discussion on the use of advanced and cognitive analytics in, please click here.