Artificial intelligence and machine learning are forging inroads in the financial services industry as firms recognize the benefits of automating key processes and making better use of existing data. As noted by Business Insider, 56 percent of banks have implemented AI in risk management, and 52 percent use these tools for revenue generation from new products and services.
However, challenges remain. While 74 percent of banking executives say these technologies will transform the industry, they’re worried about barriers to effective deployment, such as growing skills gaps and increasing complexity.
For financial firms, making the move to AI and ML requires understanding key benefits, exploring common challenges and adopting certain best practices.
The Emerging Benefits of AI for Banks
Although machine learning tools have long been used for credit scoring, fraud detection, anti-money laundering and know-your- customer applications, the advent of AI-driven natural language processing technologies now makes it possible for banks to infuse AI into a host of additional business processes. These include intelligent task automation, document processing, predictive analysis and improved customer service via personalization.
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Put simply, the self-learning nature of new AI and ML tools allows banks to analyze massive data sets quickly. Given both the increasing volume of data generated by banks and the variety of structured and unstructured data available, manual processes simply can’t keep up.
AI tools can also help reduce customer churn to digital-first fintech companies by enabling firms to deliver on-demand account access, loan approvals and investment advice.
Overcoming Hurdles to AI Deployment in Banking
The biggest challenge for effective AI and ML deployment is complexity. In practice, this complexity takes several common forms, including:
- Multiple stakeholders: There are many players involved in AI projects, including data scientists, data engineers, software engineers and deployment engineers, each of whom have their own preferences for technology tools and the way they work. Given the vast number of tools, frameworks and AI technologies available, banks often struggle to find a unified approach that works for everyone.
- Myriad processes: Considering the various processes involved in AI — data ingestion, analysis, transformation and validation, model development, validation and monitoring, and logging and training, among others — there’s substantive pressure on IT to have a forward-looking data center infrastructure or hybrid cloud strategy in place to support scalability for data science users and processes.
- Siloed data: Machine learning operations (MLOps) and data operations are now used to break down silos in much the same way that DevOps did in application development. The sheer volume of siloed data stored by banks, however, introduces complexity for uniformity and usability.
- Managing current infrastructure: Both deployment and MLOps engineers are struggling with inflexible infrastructure, a lack of uniformity and changing tools that require continual repacking and integration across bank IT environments.
This complexity can lead to delays in AI adoption at scale, in turn increasing the time between implementation and reliable ROI. The result: Both executive sponsors and organizational stakeholders may begin to lose focus and faith.
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Best Practices to Maximize the Value of AI in Banking
To make the most of AI, financial firms need component-based best practices that address different aspects of business deployment, such as:
- Infrastructure: Infrastructure adoption must be forward-looking to support scalability, performance, simplified provisioning and security of data.
- MLOps: Banks should adopt enterprise solutions that aim to operationalize ML and AI by breaking down silos, integrating workflows, delivering data integration and leveraging open-source tools and frameworks to increase deployment speed and solution usability.
- Hybrid cloud: Hybrid cloud strategy plays an important role as banks leverage both on-premises and cloud services. Container and Kubernetes strategies are beneficial to manage AI and ML across variable IT environments.
- Data architecture: Identifying and storing verified and curated data is critical to AI projects, since high-quality data makes it easier to train ML tools, in turn reducing time to market.
Artificial intelligence and machine learning make it possible for banks to reduce risk, improve customer service and automate key tasks by leveraging data resources at scale. To effectively transition from traditional to cutting-edge, however, banks must address key challenges and deploy best practices across enterprise IT infrastructure.
This article is part of BizTech‘s EquITy blog series. Please join the discussion on Twitter by using the #FinanceTech hashtag.