As global digitalization ramps up the creation of data, the financial industry needs to embrace open and collaborative data lakehouse platforms.

In the midst of widespread digitalization, as more customers expect personalized services, the financial services industry is struggling to cope with an increase in back-end processes such as data analysis, assessments, and strategic planning.

With the amount of data and knowledge that must be processed, the industry need to start leveraging AI and maximize the potential value accrued from advanced analytical approaches.

So, how can the industry take the first step in leveraging the power of AI? It all starts with building a collaborative data platform.

Getting the data architecture right

As open banking increases options for consumers, financial services organizations have to modernize their data architecture to keep up.

Currently, three data architecture options are available for efficiently storing, cleaning, and data analysis: the data warehouse, the data lake and the data lakehouse.

When it comes to what data can be stored, and how the data can be analyzed, both the data warehouse and data lake have their own strengths and weaknesses. However, the data lakehouse marries the best of both architectures, emerging as the necessary data structure for organizations to draw out crucial insights.

As the volume of data due to widespread digital transformation drastically increases, it can get stored in the wrong places—leading to data duplication and inconsistencies. This can prevent financial services firms from optimizing AI benefits to capture important insights such as fraud patterns, customer behavior patterns and investment intelligence.

To accelerate innovation and transformation, organizations should be looking at the wider potential of data: data exploration (why did something happen), predictive modeling (what will happen), and prescriptive analytics (how can we make something happen).

Benefits of AI-driven digitalization

Getting the right data architecture in place, such as a lakehouse, is the crucial first step for any organization looking to reap the rewards of using data and AI. Following that, three key areas of AI-driven data analytics can transform financial organizations for the better:

  1. CX personalization
    Providing highly-personalized customer experiences (CX) helps financial firms move away from product centricity towards customer centricity. Continuous intelligence—the marriage of event-driven decision making and historical context—ensures completely personalized interactions with customers based on the analysis of millions of unique data points every second from multiple sources.

    For example, real-time payment information is analyzed in real-time against contextual data points to drive CX. This is not about forgetting the products altogether, but innovating by looking at customer insight first, so that products align with real-time behavior histories and needs.
  2. Financial fraud detection
    As data volumes increase and online fraudsters innovate deviant tactics to avoid detection, the impact of financial fraud is keenly felt close to home, with US$83m being intercepted during a recent six-month long INTERPOL investigation on online financial crime in the Asia Pacific region, comprising mainly investment fraud, voice phishing and fraudulent invoicing.

    Having data in one place will help with scale and visibility, and will also provide an easy framework for rooting out fraud. Organizations can build a fraud detection data pipeline to visualize the data in real-time. This allows more flexibility than setting rules on how fraudsters behave and mapping this against a subset of data to detect possible fraud cases.
  3. Risk management
    To manage and respond to market and economic volatility, a modern, agile risk management practice is the way forward. While historical data and aggregated risk models run the risk of obsolescence, data and AI enable delivery of scalable, real-time insights allowing the financial services industry to address and resolve threats efficiently.

    Case in point, under an initiative known as Veritas, a 25-member consortium of leading banks and e-commerce giants have come together to evaluate their AI and data analytics-driven solutions against the principles of fairness, ethics, accountability, and transparency.

The future is open

An open, simple, and collaborative approach to data and AI will propel the heavily-regulated financial services industry forward in many ways and accelerate innovation.

Tapping the richness of customer data and its velocity can bring many opportunities for positive change and disruption, all the while keeping customer convenience and security at the heart of business growth.

As the industry pushes ahead to keep pace with the increasingly competitive and changing world, embracing ‘openness’ and ‘simplicity’ can liberate players from outdated data architectures and propel them into a space of innovative, lower-cost and consumer-centric financial services.