Financial institutions, owing to their extensive data-centricity, are increasingly adopting gen AI for better customer experience, regulatory compliance, and decision-making.
In fact, by 2038, organizations adopting this technology responsibly are expected to unlock additional economic value of over $10.3 trillion. Also, most C-suite leaders have opined that gen AI will boost the market share of their companies in the long run.
However, without gen AI-ready data, it is difficult for financial services companies to obtain accurate insights, attain desired business outcomes, and enjoy a competitive edge. Hence, this article sheds light on the key possibilities of gen AI, data-related bottlenecks, and effective solutions.
Gen AI: Top Benefits for Financial Institutions
Gen AI can help financial entities reach new heights in these key areas:
- Customer Experience
By leveraging robotic advisors and chatbots, companies can service customers more effectively and efficiently. From offering personalized financial advice to clarifying investment-related doubts, gen AI can help transform customer experience. Insurance providers can speed up claims processing by analyzing administrative data intelligently.
- Regulatory Compliance
The regulatory compliance landscape is becoming more sophisticated with every passing day. Hence, financial companies must tackle large amounts of information and complex operations. Gen AI aids in this scenario by accelerating the process of implementing new regulations as well as risk assessment and prediction.
- Decision-Making
With gen AI, financial institutions can analyze unstructured data to infer trends and market sentiment. They can prepare for sudden market fluctuations and tweak strategies if required. Companies can also develop smarter and more reliable risk models backed by detailed forecasting data and powerful algorithmic simulations.
Data Readiness: Its Importance in Financial Services
A survey by Harvard Business Review revealed that over 90% of respondents believe a dependable data foundation is essential for adopting AI. That is because regulatory scrutiny is intense in the financial sector. And poor-quality data might trigger biased credit scoring or compliance risks.
Moreover, financial institutions need to make real-time decisions and that is impossible without real-time data that is structured, clean, and scalable. Old data, after all, can render risk monitoring, fraud detection, and algorithmic trading ineffective.
Hence, before jumping onto the gen AI bandwagon, ask yourself:
- Is your data in alignment with the requirements associated with AI use cases?
- Do you have the capabilities to qualify and verify data in a way that meets AI confidence standards?
- How do you intend to govern the data in context?
Data Challenges That Prevent Financial Companies from Scaling Gen AI
In the U.S., over 60% of financial services companies lack the data environment necessary for capitalizing on AI’s potential. And these roadblocks hold them back:
- Challenging Insights Extraction
Many firms have a tough time making sense of operational data or deriving meaningful insights from the same. They fail to make informed decisions based on such data. Recruiting talent for roles like data engineer and data scientist (for quick and expert analysis) is an expensive and arduous process too.
- Lack of Ideal Data Quality and Accessibility
Unstructured data within a financial organization as well as from partners and external providers make it challenging to maintain the desired quality for deploying Gen AI. Or, financial entities bank on weeks-old data, unfit for real-time AI applications. Many institutions don’t have the infrastructure or processes to efficiently access and manage data.
- Poor Understanding of Regulations
Using enterprise data in gen AI applications requires companies to comply with specific sector and industry laws. But not all entities are adequately familiar with the same. Neither do they know how to tackle interest-based conflicts and biases. Many organizations also lack governed platforms, necessary for data privacy.
Gen AI Success for Financial Services Companies: Technology is the Key
Most financial services companies are stuck with data unsuitable for powering gen AI because they didn’t invest sufficiently in data management modernization.
Even those who tried to improve the governance, cataloging, and integration of data, depended on custom processes, legacy tools, and manual effort. However, these approaches aren’t scalable enough to satisfy the needs of digital, cloud, and AI technologies.
Fortunately, more and more financial entities are realizing that data is an asset and not just a transactional byproduct. It requires careful curation, management, and governance.
Hence, to make your data gen AI-ready, identify the business outcomes you desire and invest in the following solutions:
- Data Integration
With these solutions, you can get your hands on essential data of any structure, format, or volume, and from any source. This is helpful for putting together, training, and implementing gen AI models as well as systems that support them at scale.
2. Data Governance
Governance technologies enable financial companies to define data policies and enforce the same. These solutions also boost data literacy across the organization and ensure that only authorized individuals can access sensitive data appropriately. Such technologies keep data breaches at bay and protect companies against regulatory penalties.
3. Data Quality
With these solutions, financial services firms can spot errors, resolve them, and gain visibility into the existing condition of data quality. Simply put, you can put your faith in the data that your systems and applications hold.
4. Data Catalog
Such technologies help users to understand the source of data, its intended use, if it’s accessible to certain applications or systems, and if the data is secured. Hence, by introducing transparency, data catalog solutions allow business users and technology experts to decide what the data undergoes from creation to consumption.
5. Master Data Management
Through these solutions, financial organizations can create a unified source of truth about accounts, customers, services, and counterparties. You can also derive insights about how everything is interconnected, so that operational and analytical systems can leverage the same.
6. Data Marketplace
When required, data consumers across a business can use these solutions to access data at its source. Moreover, you can gain access to data information as well as scorecards on quality.
Scale Gen AI in the Financial Services Sector with Advanced Data Management
Sure, the growing urge to embrace and scale gen AI is good news for both financial services providers like you and end users. However, it is vital to ensure that your data is fit for business. In other words, leveraging modern solutions to manage and govern data is the need of the hour.
So, choose solutions that are completely and seamlessly integrated. Also make sure they are cloud native and driven by machine learning and AI. Partnering with the right data management solutions provider can help you handle data assets cost-effectively and efficiently.
To drive business growth with gen AI, reliable, high-quality, and properly-governed data is integral. And the right data management solutions can deliver the productivity and scalability you seek.
The post Financial Services Are Racing to Scale GenAI – But Their Data Isn’t Ready Yet appeared first on Datafloq.
