June 18, 2019 | Written by: Mina Wallace
Categorized: AI | Banking | FinTech | IBM RegTech Innovations
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In this series of blogs, we will focus on four transformative technologies with emerging risk applications that can help banks and financial institutions grow profitability and protect the enterprise. Each technology is at the start of an enormous adoption growth curve, and has been the subject of intense discussion. In this edition, read how these tools are being deployed now.
Risk brings rewards
Risk management professionals are comfortable with ideas about growth curves and early versus late investment. In general, early entry in a high growth cycle can mean high rewards, while late entry can mean lower rewards — or none, if the opportunity is missed. With certain fast-growth technologies, financial organizations should aim for early entry to reap the rewards.
In the IBM white paper “A new era of technology-enabled financial risk management,” discover in greater detail how to apply emerging technologies to help modernize risk management capabilities.
Of course, a key benefit of technology adoption is transformation. For financial institutions, transformation is about modernizing outdated risk systems and optimizing infrastructure in order to deal with issues threatening the ability to prosper, including:
- Increasing regulation,
- Higher expectations for transparency and profitability,
- Out-of-control growth in data volumes, and
- Increasing sophistication.
Advanced analytics and aggregation, cloud and AI, and big data
With regulation increasing, data growing in both volume and sophistication, and stakeholder expectations at an all-time high, the advanced analytics and aggregation needed for stress testing often pushes traditional infrastructure beyond its limits. New risk reporting and analysis technology helps institutions consolidate risk data and analytics, seamlessly accessing information from various systems across the organization. With a different, more capable approach to risk aggregation than traditional tools, it provides near real-time performance for data aggregations, even at the most granular levels. Advanced analytics and aggregation technology empower users to create highly customized calculations to meet new financial regulations and internal management reporting.
Moving to cloud from on-premises can deliver a host of benefits, not the least of which are cost, flexibility and scalability. With open-source cloud software, the true value and flexibility of cloud is made possible. Docker and Kubernetes are container technologies that together help to create portable application containers and to manage them across cloud or on-premises. These assets enable users to create applications quickly, with greater data portability across multiple public and private clouds. They also allow use of unique cloud-native security features which historical, legacy IT doesn’t have.
The potential of artificial intelligence for financial application is limitless. It augments and amplifies human performance. Cognitive technology can be used to uncover patterns in identifying new stress-testing scenarios—faster than ever before. Natural language processing finds and understands patterns and connections in news and blogs. It even identifies changing sentiments—before they hit the news. With intelligent automation, machine learning, continual optimization, decision insights delivered from across systems, vendors and platform, the list of improvements that AI can deliver is extensive. Yet each item represents an essential competitive capability.
Risk management demands a lot of data from many different sources, and traditional database management systems are too slow for the granular analytics needed today. A big data approach, among other things, helps run huge, complex volumes really fast. It helps run small, complex volumes incredibly fast. IBM worked with the center of excellence team at a large Canadian bank to implement a big data approach for liquidity stress testing. Client results from that project showed that a four-hour processing run time could be cut to just a few minutes, which indicates big data may be ideal for asset liability management and market risk management applications.
What you don’t know can hurt you
Firms that wait to adapt to these technologies may find themselves playing catch-up. Challenges to adoption include delays over learning curves, IT and system complexity, and migration architecture and planning.
In the other editions of this series, we look at how each technology—advanced analytics and aggregation, cloud and AI, and big data—can be successfully used to help transform risk management. Explore all these topics in the IBM white paper, “A new era of technology-enabled financial risk management.”