While some trends come and go, the latest age of data has seen three consistent tendencies that inform how financial service organizations tackle digital transformation, outside of master data management.
- Cloud-based solutions for storing and processing data
- AI and ML for making sense of it all
- Data governance and compliance for managing quality data and privacy
1. Cloud-based solutions
Leveraging cloud-based solutions for storing and processing large amounts of data allows financial institutions to scale their operations more easily and reduce costs. Cloud providers – like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) – offer a variety of solutions for data management, analytics, and machine learning that can be tailored to the financial institution’s specific needs.
Overall, cloud solutions can help financial services companies increase efficiency, reduce costs, and improve the quality and security of their services.
- Scalability: The financial services industry is subject to fluctuations in demand, and cloud solutions allow for quick and efficient scaling of resources to meet changing business needs.
- Cost savings: By using cloud services, financial services companies can reduce the cost of maintaining and upgrading hardware and software.
- Increased security: Cloud providers offer robust security measures and invest heavily in security infrastructure, which can often provide better security than on-premise solutions. According to the IBM X-Force Threat Intelligence Index, the financial services industry had the second-highest number of data breaches in 2021 – making security a key feature behind the adoption of cloud-based solutions.
- Improved compliance: Because cloud providers are often required to comply with regulations such as SOC 2, PCI DSS, KYC and GDPR, it can simplify compliance efforts for financial services organizations.
- Enhanced data management: Cloud solutions provide centralized data management, enabling financial services companies to quickly access, analyze, and utilize their data more effectively.
We’ve accelerated these benefits with clients assisting one to achieve $1.3M annual cost reduction by moving from on-premise to cloud and reduce data ingestion from hours to minutes, improving productivity by 73%.
2. AI and ML
Employing artificial intelligence (AI) and machine learning (ML) to analyze and make sense of the vast amounts of data being generated supports organizations in fraud detection, risk management, and personalized financial advice (AKA enhancing customer experience).
AI and ML are being used to enhance and enable faster customer onboarding, driving higher levels of satisfaction. What does this look like?
- ID verification: AI-powered identity verification solutions quickly and accurately verify a customer’s identity, reducing the time and friction involved in the onboarding process.
- Customer profiling: AI algorithms analyze customer data and create a detailed customer profile, which can be used to personalize the onboarding process and offer more relevant products and services.
- Risk assessment: AI assesses the risk associated with new customers by analyzing their financial and behavioral data (i.e., credit scores and loan exposure) to determine their creditworthiness.
- Document analysis: AI automates the process of analyzing and verifying customer documentation, reducing the time and effort required to manually review and approve documents.
AI and ML are also used to analyze customer behavior and preferences, allowing them to personalize their marketing and sales strategies, increasing cross- and up-sell opportunities. Lastly, back-office operations like loan processing and compliance checks can be automated, freeing up staff to focus on more complex and higher-value tasks.
Advanced analytics can help organizations drive insights, make more informed decisions, and improve operational efficiencies. These benefits help companies stay competitive in an ever-changing market and meet the needs of their customers in evolving ways.
We’ve installed machine learning platforms for clients, enabling data scientists with 3X faster decision-making.
3. Data governance and compliance
A 2020 study found that 74% of financial services executives believed regulatory pressure to be a major driver of data management initiatives.
Data governance and compliance are top of mind as financial institutions must continue to comply with regulations surrounding data privacy and security, like GDPR and CCPA. Governance and quality tools ensure data is accurate, complete, and consistent – tracking, monitoring, and reporting data quality issues. Enterprise-wide data governance enables better use of data, improving operations, enhancing decision-making, and increasing revenue.
The importance of data governance in the financial services industry is driven by several interwoven factors, in addition to regulatory requirements:
- Customer trust: Data governance helps ensure that customer data is protected and used appropriately.
- Data quality: Strong data quality is essential for making informed decisions and providing high-quality financial services.
- Data privacy: Keeping data protected and private is critical for maintaining customer trust and avoiding reputational damage.
These tried-and-true trends are key to digital transformation efforts for financial institutions and bring a range of benefits from increased efficiency, agility, and innovation, to enhanced customer service and risk management.
Are you on trend? Learn how to maximize the use of transformative tools to help you stay competitive, improve operations, and gain a competitive advantage with Paradigm’s team of Digital Transformation experts.
By Chris Gately, Chief Revenue Officer