A recent McKinsey report says that global and regional banking leaders increasingly rely on digital ecosystems driven by artificial intelligence (AI) and analytical capabilities to inform their business processes. To that end, the main challenges banks face today stem from their inability to operationalize vast amounts of data and deploy advanced analytics.
Banks that successfully integrate advanced analytics and data operationalization into their technology infrastructure hold a significant competitive advantage over banks that rely on manual processes. Although transitioning to digital, data-driven systems can be challenging for banks, the benefits are tremendous – product personalization, faster pricing processes, enhanced risk management and more.
Before the rise of AI in banking, there was a dependency on the consumers to collect the information needed to determine the right product and price. These manual processes, which some banks still employ, are prone to errors, and that is only one of the many downsides. Frequently, there is either not enough data or inaccurate data to inform important banking decisions. This leads to consumers getting standardized or inadequate product offerings.
The alternative to manual processes is AI-driven systems that enable automation from the point of price and product creation to deployment. If integrated properly, AI can determine personalized products and prices using sophisticated, machine-learning (ML) models. It can also leverage vast data sources, operationalized through an end-to-end, analytics-driven system in real time.
In practical terms, AI-driven decision-making enables banks to personalize their offerings to consumers’ circumstances at any given time. More advanced AI-based systems enable banks to proactively provide consumers with product recommendations and tailored product offerings. Automated and integrated data and analytics solutions can achieve compliance through design and scalability, continually improving the accuracy and speed of models by leveraging ML capabilities.
There are three things banks can do today to operationalize vast amounts of data:
Make AI more accessible to the business side
One of the biggest challenges banks face is obtaining the diverse set of skills to implement analytics-driven solutions. The data scientists build the models and simulations, which product managers use to determine the best offers. But scientists do not always have visibility into the business challenges that need to be solved through operationalization and automation. Conversely, banks have experts who understand the business needs, but not the technical expertise to leverage AI to achieve objectives. Therefore, banks can benefit from making AI accessible and more understandable to the business decision makers.
Create transparency throughout the pricing process
AI-driven data processes can raise concerns among risk management and governance professionals who may be cautious or have concerns about compliance and governance. Deploying AI-driven systems that can ensure governance and compliance over the entire pricing process is key. To ensure that a system can deliver on this front, banks need model transparency and the ability to trace all consumer proposals throughout the lifecycle, including the data and the models that informed the decision-making process.
Use an agile framework across business lines
Lastly, pricing and product development often occur in silos, with data, tools, and resources confined to individual business lines. Combining and leveraging analytical methods and models across organizational structures requires an agile and unified framework to be adopted, one platform that all relevant internal stakeholders use and offers a single, and full, view of the consumer.
To mitigate the challenges, banks need a well-developed strategy in combination with a software solution that can be seamlessly incorporated alongside existing models, data and legacy systems.
Banks should consider adopting a solution that aligns performance, risk management and business objectives and can automatically connect data from across the institution to inform analytics-driven pricing and product decisions. This is how data operationalization is achieved. Additionally, to ensure a smooth transition from manual to data-driven automated processes, banks need to break down organizational silos and invest in building institutional knowledge about the use of advanced analytics. By doing so, they can make better-informed business decisions.
A platform that enables banks to accurately respond to the consumers’ needs will also help ensure their business objectives and KPIs are met. Although the process of integrating systems that leverage data and analytics can be challenging, the benefits and competitive advantages banks earn are evident and can be achieved long-term.
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