Financial services (FSI) is an ultra-competitive industry where both emerging and established businesses are searching for innovative approaches to leveraging their enterprise data, and strengthening their position in the marketplace, and future-proofing their business models.
But while some financial service businesses have been quick to adopt the most innovative cloud-based and AI-driven data strategies, many other firms still operate in a realm of data chaos encumbered by their dependance on legacy systems. Because these organizations take longer to generate data-driven insights and often fail to leverage enterprise data to its full potential, it is becoming increasingly difficult for them to keep up with their competitors and monetize their data assets.
Still other firms, meanwhile, recognize the imperative of upgrading their data architectures, but have found themselves on what we might call a data migration treadmill: moving from one out-dated data architecture to another, constantly shifting analytics strategies, but never truly getting ahead of the curve or achieving strategic data goals.
In this blog, you’ll discover how businesses in the financial services industry are leveraging their data to improve customer experiences and drive profitability. We’ll also explore the reasons why FSI businesses might be unprepared for a data-driven future, and how these businesses can begin future-proofing their data strategies in Snowflake with help of emerging AI technologies.
FSI Organizations Have Access to Huge Volumes of Data
FSI firms have access to massive volumes of data that can be aggregated, processed, and analyzed to extract valuable insights that impact decision-making and drive business outcomes. Some of this data includes:
- Customer Personal Data, such as personal details (e.g. name, address, contact information, education and employment history), and demographic data (e.g. age, gender, income), along with account balances, preferences, and behavioral data.
- Customer Transactional Data, such as details about transactions made by customers (e.g. deposits, withdrawals, loans, payments, purchases, trades, etc.).
- Customer Credit Data, such as data about each customer’s credit history, reports, loan applications, open credit accounts, repayment history, and credit score.
- Financial Market Data, such as current and historical data about financial markets (e.g. bond prices and yields, stock values, exchange rates, trading volumes, commodity prices, etc.).
- Operational Data, such as security, event, and user behavior logs from web-based financial service applications.
With the right data architecture, infrastructure, and analytics capabilities, FSI companies can transform this data into valuable insights that shed light on customer preferences and behavior patterns, detect and prevent fraudulent usage, enhance trading strategies, or support regulatory compliance objectives. But without the right data architecture and capabilities, financial institutions may struggle to access their data and utilize it effectively.
Many FSI Organizations are Unprepared for an AI Future
There are plenty of opportunities available for FSI firms to leverage data into enhanced business outcomes. The most cutting-edge financial services companies are already adopting AI-driven technologies to help leverage their data, but there’s a huge number of FSI companies that haven’t adopted these technologies yet and are currently unprepared to embrace the opportunities of AI.
Consider the following reasons why some FSI companies aren’t ready to start leveraging AI-based applications – how many of these apply to your business?
Legacy Systems and Technology Challenges
Many established financial services companies are still hosting critical applications and databases on outdated infrastructure, including mainframe computer systems that were implemented 30+ years ago. These legacy systems usually lack the capability to affordably deal with large volumes of data and they don’t integrate well with modern cloud-based analytics platforms or AI-driven applications. As a result, organizations who depend on legacy systems are missing out on opportunities to leverage innovative new technologies.
Financial services companies that have yet to migrate their data and applications into the cloud often face significant challenges around infrastructure scalability. While public cloud providers allow their customers to scale data storage, compute resources, and analytics capabilities as needed, firms operating their own data centers have much less flexibility to scale resource requirements without a large capital investment.
Data Quality and Accuracy
Financial services firms collect data from a variety of different sources. Having many data sources is generally a good thing, but it can also create confusion and negatively impact data quality when those sources conflict. To overcome data quality issues, organizations need to aggregate and normalize the data into a single source of truth that can serve as the basis for downstream analytics operations.
Data Governance and Transparency
Without the right systems in place to manage their data, firms often run into challenges around data governance. These can include things like:
- A lack of formalized processes or accountability for data governance
- Trouble enforcing data governance policies across the organization
- Difficulty integrating data from disparate systems and external/3rd-party sources into a single source of truth
- The inability to accurately track data access, usage, and data lineage.
Poor data governance practices create compliance risks and make it harder for a business to effectively use its data.
Regulatory Compliance Complexity
Financial services firms face some of the most stringent regulatory compliance requirements of any industry, especially when it comes to data privacy and security.
Finservs store personal data from their customers along with credit card and bank account numbers that must be safeguarded against unauthorized access. To make use of this data, organizations must implement systems to collect, aggregate, store, process, analyze, and share the data while ensuring compliance with data privacy laws and regulations.
Delayed Access to Data
Many financial services companies are still using legacy technologies to build and maintain the data pipelines they use to aggregate and process data. Maintaining data pipelines to facilitate data integration can take hours every week from highly-skilled data engineers, whose skills could be better put to use in other areas.
Data pipelines can take weeks to build and hours or days to execute, delaying access to critical data and preventing organizations from analyzing their data in a timely fashion to support business decision-making.
Future-Proofing Your FinServ Data Strategy with AI
AI-driven applications are changing how Finserv businesses can leverage their data to enhance business outcomes. Finserv companies with the right data infrastructure to leverage the cutting-edge capabilities described below will find themselves ahead of the curve and competing for market leadership in an industry that will be increasingly shaped by the emergence of AI.
AI-driven Data Discovery
For Finserv firms with low data maturity, the first challenge is just to understand where data lives and how it’s being used in the organization. AI-driven data discovery makes it easy to unravel the mountain of reports and dashboards across the organization and figure out where data lives, who owns what views of the organization, and how metrics are calculated.
AI-Powered Risk Assessment and Modeling
Finserv businesses, including retail and investment banks, insurance companies, and asset/wealth management firms, can now conduct risk assessments using specialized blockchain and AI-driven tools. These tools analyze large datasets to build accurate predictive models for creditworthiness and insurance/investment risk.
AI-driven Cybersecurity and Fraud Detection
Cybersecurity and fraud detection are two major areas of impact for AI-driven applications in the Finserv industry. By analyzing security and transactional data in real time and at scale, AI-driven security monitoring tools can help Finserv SecOps teams identify cyber incidents or data breaches in progress, or detect suspicious/anomalous activity that might indicate fraud.
AI-driven Data Pipelines
Before data can be analyzed effectively, it needs to be aggregated and normalized into a single source of truth to ensure data quality and integrity. AI-driven data pipelines streamline the data aggregation process, unifying multiple data sources while freeing up valuable time for data engineers to focus on more impactful and value-generating activities than building and maintaining data pipelines.
Financial services are leveraging AI-driven predictive analytics to identify risks, optimize lending decisions, improve customer experiences and targeting, and enhance asset allocation. Investment firms can also use predictive modeling to support and enhance trading algorithms, specialized programs that automatically buy/sell assets based on real-time market trends and deep insights from historical data.
Start Leveraging AI in Your FinServ Data Strategy with Hakkōda and Snowflake
Migrating to the cloud and building a modernized data architecture are necessary prerequisites for Finserv businesses hoping to take advantage of emerging AI-based software technologies. Moving data and workloads into the public cloud gives Finserv companies acccloud daess to highly scalable and durable cloud infrastructure, along with powerful cloud analytics platforms like Snowflake.
At Hakkoda, we help our clients in the Finserv business migrate critical workloads into the cloud where they can get off the data migration treadmill and leverage solutions like Snowflake’s Financial Services Data Cloud to enable AI-driven technologies across multiple use cases.
Ready to learn more? Contact our data experts to discover how Hakkoda can help your Finserv business compete more effectively with an AI-driven data strategy while future-proofing your data strategy for years to come.