Sample financial services outcomes
and the initiatives that support them

Financial services institutions have a tremendous number of data and AI use cases. However, when planning or executing use cases, five distinct outcomes rise to the top. All of these use cases are important to enhancing customer experiences, reducing risk and driving higher revenues.

Collibra tackles financial services' toughest use cases

Optimize operational
efficiency

Process automation
AI-driven analytics
Infrastructure and asset management

Drive transformation
and innovation

Product lifecycle management
Market trend analysis
Mergers and acquisitions

Enhance financial crime
detection and prevention

Data privacy management
Cybersecurity risk analysis
Fraud detection and prevention

Enhance customer
experience

Customer 360 views
Personalized marketing
and product offers
Cross-channel customer service

Improve risk management
and compliance

Regulatory compliance
for data and AI
Credit risk analysis
Audit trail management

Outcome: Improve risk management and compliance

For financial services institutions, improving risk management and compliance is particularly important due to heavy regulations and customer perception. The ability to identify and manage potential risks, whether they be financial, operational, market or others, is critical for sustained growth and profitability. Compliance with regulations including financial specific regulations like BCBS 239, as well as data and AI specific regulations like CCPA and the EU AI Act, helps keep organizations out of the headlines and reduces financial penalties. With the right capabilities to have full confidence in data, risk management and compliance can become tools to help financial institutions shine in a crowded market.

Regulatory compliance for data and AI

Limited understanding of the risks and impact from changes, and issues on data consumers: Without understanding the risks and impacts of ever changing and evolving data, consumers of that data, whether internal or external, will have difficulties making decisions, leading to poor analysis, misguided decisions and even non-compliance with laws and regulations
Lack of a central data repository: Without a central repository to catalog vital data elements, it's challenging to track, manage, and utilize this data effectively, leading to inefficiencies, inaccuracies, and potential regulatory breaches
No ability to document business and technical lineage: The inability to fully understand where data comes, how it moves between systems and how it’s used can severely hamper transparency, leading to inaccurate data and non-compliance with financial, data and AI requirements and regulations
Lack of data usage guardrails and access controls: Sensitive financial and customer data requires extraordinarily well defined levels of security and handling – without this level of scrutiny could come violations of regulations, data breaches and loss of customer trust

Decreased reputational risks: Effective compliance for data and AI laws and regulations can significantly decrease reputational risks, helping you keep current customers, attract new business and reduce loss of revenue
Decreased regulatory fines/penalties: By staying compliant with financial, data and AI laws and regulations, your organization significantly reduces exposure to fines and penalties which can be significant, as well as loss of revenue from reputational damage
Decreased external audit costs: The cost of external audits can be exorbitant, however with the proper capabilities in place to stay compliant, the time and level of effort of audits is decreased, leading to lower overall associated costs
Increased productivity: By continually monitoring and staying in compliance, financial institutions are able to streamline and automate processes, helping to reduce the burden on compliance and data teams so they can focus on other work and enhancing efficiency elsewhere

Outcome: Enhance financial crimes detection and prevention

Financial institutions face nothing but positive gains by enhancing their ability to detect financial crimes. Identifying and preventing illicit activities such as fraud, money laundering and embezzlement all come with upsides including maintaining trust with customers, staying out of legal and regulatory trouble, and reducing financial losses. High-quality, trusted data sits at the heart of detection and prevention, with sophisticated AI models and financial forensic techniques being used to mitigate unlawful acts.

Fraud detection and prevention

Inconsistent, siloed and low-quality data: When internal teams only have access to inconsistent, siloed or low-quality data, it makes it extremely difficult and increasingly time consuming to analyze patterns and identify fraud or illicit activities, increasing the risk of financial damages
Difficult to manage fraud detection models: As criminals vary, diversify and intensify tactics, institutions with inflexible fraud detection models and methods will struggle to adapt and combat these new fraud tactics, resulting in financial losses and damage to their credibility
Lack of data insights: Lack of data insights as a result of poor or low-quality data can lead to the unreliable detection of fraud, causing financial loss and regulatory penalties
Lack of proactive data monitoring: Early fraud detection relies on the proactive monitoring of data, helping to minimize potential losses, regulatory penalties and reputational damage
Reduction in losses: Robust fraud detection powered by high-quality, reliable data and AI helps reduce losses by enhancing analysis to quickly and confidently identify fraud
Faster fraud detection: With the right AI models fed by high-quality data, financial institutions can save vast amounts of time and money by analyzing and identifying fraud faster
Decreased reputational risks: While fast and efficient fraud detection and prevention can mitigate financial losses due to compliance reasons, it can also help protect losses from reputational damage by demonstrating robust and proactive measures to eliminate fraud
Increased productivity: Automating fraud detection with AI tools and workflows can increase employee productivity by reducing or even eliminating manual tasks and reducing erroneous findings

Outcome: Enhanced customer experience

The relationship a customer has with an organization, especially a financial institution, can be derailed quickly when poor experiences happen. Customer experience (CX) is much more than good customer service – it’s having a complete understanding of the unique needs of each customer with the ability to serve them across multimodal channels, creating lasting impressions and positive experiences. Having the right data, delivered at the right time to the right person to create those positive CX moments sets leaders apart, improving customer satisfaction and increasing revenue.

Cross-channel customer service

Inconsistent data across channels and sources: Customer data is created at numerous touchpoints and often siloed systems with inconsistent formats, leading to poor analytics and bad CX. This data needs to be consistent to ensure accurate analysis and decision-making
Delayed access to critical information: Without the right data at the right time, customer service interactions can be negatively impacted, leading to customer dissatisfaction, missed opportunities to upsell products and services or loss of business
Compliance and privacy concerns: Financial institutions handle extremely sensitive personal and financial data, and ensuring compliance with laws like GDPR while providing omnichannel experiences is paramount to ensuring customer trust
Improved customer satisfaction: With cross-channel customer service driven by timely and accurate data, financial institutions can better serve their customers, resolving issues faster, delivering personalized offerings and improving overall satisfaction
Optimized marketing spend: By using data from various channels integrated into a single pane of glass, financial organizations can identify the most effective platforms and strategies for reaching their target audience more efficiently, helping to decrease spend and provide a higher ROI on marketing campaigns
Competitive differentiation: With high-quality cross-channel data, financial institutions can gain deeper insights into customer behavior and needs, offering innovative products and services to stay ahead in a highly competitive market
Increased productivity: Trusted cross-channel customer service data can lead to huge productivity and efficiency gains by reducing time to resolution for customer service, products and services can be delivered to specific segments more effectively and more

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Ensuring productivity and efficiency for your data and AI initiatives is key. Collibra is here to help guide you with capabilities and solutions to tackle any use case your organization needs.

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