Despite the financial industry’s gradual adoption of Artificial Intelligence (AI) and Machine Learning (ML), the potential for organizations to boost their data capabilities remains undeniable. The latest LSEG Labs AI/ML Study sheds light on how companies can bridge the gap and tap into the wealth of data-driven opportunities.
Embracing Alternative Data
An increasing number of financial companies are delving into alternative data sources, posing new challenges in data integration and utilization.
The Opportunity for Industry Leadership
With only 45 percent of companies employing AI and ML in multiple areas, the potential for businesses to emerge as industry leaders in AI/ML becomes more pronounced.
The Core-Satellite Hybrid Model
Institutions are increasingly adopting a core-satellite hybrid model for AI/ML adoption.
A Growing Divide
A significant gap is emerging between companies with robust data capabilities, enabling them to spot opportunities and act on them, and those lagging behind.
The Impact of COVID-19
The COVID-19 pandemic has heightened the demand for real-time data to make informed decisions, emphasizing the importance of data-driven strategies.
Key Findings from the 2021 AI and Machine Learning Study
In the 2021 AI and Machine Learning study, 55 percent of surveyed firms are only using ML in one area or not at all, while the remaining 45 percent are deploying it across multiple areas.
Joining the AI/ML Revolution in 2022
It’s not too late to embrace AI/ML, and you’re not alone. AI/ML adoption has become more accessible over the past few years.
- Assess Your Starting Point
Begin by honestly assessing your current AI/ML maturity and capabilities. While many companies perceive themselves as leaders or challengers in AI/ML, few have truly secured those positions.
- Seek Assistance
Consider seeking external AI/ML services and leverage third-party vendors that integrate with your systems. Out-of-the-box tools can be suitable for generic tasks, but bespoke solutions may be needed for specific problems.
- Embrace an Iterative Process
Success in AI/ML depends on adopting a process that continuously delivers, monitors, and adjusts results. A repeatable, end-to-end process is more likely to lead to success than relying on a single ‘silver bullet.’
- Partner with Experts
Collaborate with subject matter experts and focus on data rather than technology. A hybrid model for data science teams, with central and smaller embedded teams, can be effective.
- Prioritize Data Quality
Data quality is a distinct discipline that should not be left solely to data scientists. If you have an ML strategy but no data quality strategy, it’s crucial to revisit your approach. Systematic data problems and dataset linking are significant pain points to address.
In Conclusion
The financial services industry holds ample opportunities to harness AI and ML for data enhancement in 2022. Understanding your current position, seeking assistance, embracing an iterative process, partnering with experts, and prioritizing data quality are key steps to seize the potential that AI/ML offers.