AI in FinTech - Data Readiness for AI Solutions

The future of FinTech is here!

And it is with AI!

But…

How to Manage AI Risk?  Join in, and let's explore together.

By now, we all know that AI is revolutionising FinTech and other businesses. The evolution and adaptation of AI are in their infancy at this stage and are picking up pace within a short duration.  To illustrate the pace, almost every FinTech and traditional financial institution, if not all, has some AI program and prototype in the making.

With the adaptation of AI, it is clear that one prerequisite is vital for the AI program to be successful in the long run.  The most essential prerequisite for a reliable, solid AI solution is tonnes of high-quality underlying data.  AI solutions use the underlying data to come up with outcomes.  If the underlying data used by the AI solution is sub-optimal (Incomplete, error-prone, and biased, to name a few), then the result is also poor.

Data and AI

In this section, let's explore why the AI solution requires data.

• Training: AI solutions generate outcomes based on the training from the underlying data set.  The quality of the solution is directly proportional to the quality of the underlying data.
• Decision Making: Recently, AI solutions have provided several critical decisions. For example, FinTech and other traditional banks increasingly use AI tools to approve or decline loans.  This decision-making is accurate only when the AI solution provides reliable information.  AI solution (or model) will only generate reliable output when trained on reliable input(s).
• Risk Management: Data-driven insights are imperative to identify and mitigate risks.

Challenges with Data

In this section, let's review the challenges with data.

• Data quality: It is challenging to confirm that the underlying data is accurate, consistent and complete.
• Data availability: The organisation relies on publicly available data such as stock prices and proprietary data that the FinTech firm owns.  Suppose the organisation is inactive in a specific market or doesn't contain enough data because they do not trade that product or for some other reason. In that case, they may not have enough data to train their model.
• Data Integration: When the data passes through multiple systems, with each system holding the data in different formats, integrating and understanding data lineage is very difficult.
• Data Privacy and Security: When protecting sensitive data, unauthorised breaches or accidental emails to external users are some of the vast issues without AI.  Data privacy and security were issues well before AI was in the mix. With AI now, the problems are multiplying manifold.

Data Readiness for AI

In this section, let's determine how data should be ready to exploit the AI boom.

• Data Lineage - establishing and managing lineage systematically with all the data transformations at multiple stages, including various systems, will assist with data transparency.
• Data Accuracy: When there is transparency with the origin of data and a thorough record of all transformations, quality control is applied to ensure inconsistencies are understood and corrected, and manually capturing missing data ensures the underlying data is curated and accurate.
• Data Governance: Besides AI governance, data governance is essential to confirm data quality, security, ethics and compliance. This feeds into AI governance, as the accuracy of underlying data is critical to the AI models.
• Data Literacy: Sufficient training is required so employees have the skills and knowledge to work with data.

For example, when the underlying data from various internal sources and external data vendors are accurate and well-governed, AI can do its magic by generating more reliable outcomes.  Organisations (big and small) do a great job with data management and can undoubtedly raise competitive advantage.

References

1. Data Readiness for AI: 4 Fundamental Factors to Consider (atlan.com)

2. Transforming AI Outcomes with Effective Data Readiness | Deloitte US

3. AI Data Readiness: The Ultimate Guide to getting data ready for AI (ciopages.com)

4. Data Discovery: The First Step to AI Readiness - Microsoft Community Hub

5. The path to AI-ready data | Deloitte US

6. Assessing AI Data Readiness | Synergise AI (synergise-ai.com)

7. Why Data Readiness is critical for winning AI Deployments (itbrief.com.au)

8. Navigating Data Readiness for Generative AI - DATAVERSITY

9. Data readiness unlocks the potential of AI | TechTarget

10. Is Your Data Infrastructure Ready for AI? (hbr.org)

11. AI Readiness Index - Oxford Insights

12. Quick Answer: How to Build and Sustain an AI-Ready Data Management Practice? (gartner.com)

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Did you find the article informative?

If you liked this, you may also find the previous articles in this series interesting. Here are the links:

1. AI in Fintech - Exploring Regulatory Landscape of AI Governance - Framework and Approaches

2. AI in Fintech - The Power of Large Language Models (LLMs)

3. AI in FinTech - Ethics in Financial Markets

4. AI in FinTech - The Future of Human-AI Collab

5. AI in FinTech - Explainable AI (XAI)

6. AI in FinTech - AI Risk Management - Part 1

7. AI in FinTech - AI Risk Management - Part 2

8. AI in FinTech - Building a Robust AI Security Framework

9. AI in FinTech - Continuous Monitoring for AI Risk Management

10. AI in FinTech - Taming the Risks, Seizing the Rewards

11. AI in FinTech - Build a Culture of AI Governance Beyond the Checkbox

12. AI in FinTech - The Future of Security to Protect Data and Systems

13. AI in FinTech - The Impact on Financial Inclusion

14. AI in FinTech - Microfinance for Financial Inclusion

15. AI in FinTech - Financial Literacy for Financial Inclusion

16. AI in FinTech - Financial Inclusion in Remittances

17. AI in FinTech - Financial Inclusion with Micro-Insurance

18. AI in FinTech - Anti-Money Laundering (AML)

19. AI in FinTech - The Rise of Generative AI

20. AI in FinTech - Generative AI Implementation

21. AI in FinTech - The Role of Central Banks in AI Governance

22. AI in FinTech - Need for Robust Governance

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