AI has penetrated deeply into the financial realm, influencing diverse applications from back-office automation in fintech firms to strategic decision-making in traditional financial services.

In insurance companies, AI navigates through claims processing, underwriting, and pricing, influencing operational efficacy and customer experiences.

The likes of JPMorgan Chase have already identified numerous use cases, pointing towards a future where AI is not merely an option but an integral component of financial operations.

Knowing what AI is is one part of the job; using it responsibly is another. While most people are focusing on how to adopt AI in their daily tasks, most of them forget about the responsible adoption of AI. Especially in Finance, where regulations are ruling, you need to consider this from day 1.

As with any new technology, be it the internet, robotics, or AI, unless planned correctly and carefully, first initiatives could backfire and evolve into an uncalculated risk and expensive experiments that don’t deliver the expected value.

With Generative AI (GenAI), it isn’t different. There is a high risk of underestimating the extent to which organizations and people must adapt to adopt the value of GenAI in their daily business. Given that the technology is continuously evolving, companies are at risk of adopting GenAI in its early stage and taking the wrong path, as the whole picture is still to be clarified.

The key is to find the right balance between innovation, carefulness, and responsibility.

The shift GenAI is enabling brings fear of losing their job through automation. Companies, executives, and stakeholders are responsible to their employees to ensure that GenAI isn’t a threat.

It is an opportunity for each of them to use their time smarter and more effectively, ultimately enabling more return on investment (ROI).

Not only this, inaccurate or biased algorithms can be highly problematic when they’re in use directly by the customers, but things can get much more severe and worse when they’re deciding whether a person is eligible for a credit card limit increase, receiving a car or house loan, or doing wrong investments.

Any bank deploying AI in one of those domains has ultimately been sure to have a responsible AI plan and proper safeguards to reduce the exposure of failure and limit itself against many potential problems.

All those mentioned reasons are just pathing the way for what I’ll tell you now.

The Five Design Principles of Responsible AI in the Era of GenAI

Responsible AI is designing, building, and deploying AI in a manner that empowers employees and stakeholders to adopt it reasonably to bring better customer services to everyone.

A personal strategy to adopt responsible AI

A comprehensive approach to responsible AI involves identifying areas where problems could arise before deploying AI models. The question then becomes how you approach those areas practically while the business runs.

In reality, this isn’t thought in academia. This isn’t something you learn through books or practical examples. It is something you know in life.

Therefore, let me share my approach to understanding what’s needed to adopt a responsible AI strategy in the era of GenAI or AI.

1. Be aware of all risks, such as model bias, security flaws, inaccuracy, and usage of ethical or personal variables in your models.
2. Have clear guidelines about the ethical usage of AI models within your company.
3. Have clear guidelines for AI governance.
4. Provide education to your employees by training them and providing access to AI experts.
5. Continuously assess the risks, identify new risks, and solve them through standards or procedures.
6. Establish and adopt the process of responsible AI in the team of AI experts and company-wide.

You might have thought those are the five principles of responsible AI in the era of GenAI. You might have seen that I listened to 6. Those are just the strategies to adopt in daily business. The principles I will explain in a minute. First, let me tell you something else.

Responsible AI is a “Double-Edge Sword”

AI is changing the game in financial services, causing companies to compete using new technologies. However, rushing to use AI without enough thought can lead to several issues, like unintentional biases and misusing data, which can cause companies to break the rules.

Even though big tech companies are letting go of their ethics teams, this should inspire other companies to work even harder to use AI responsibly. This means diving deep into understanding how to build trust in AI. A well-thought-out plan can help companies use this powerful technology to its fullest while avoiding the potential problems it can bring.

It’s essential to ensure we can trust AI, balancing the fantastic things it can do with ensuring it’s used correctly and safely.

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5 Design Principles of Responsible AI

At a Glance:

1. Be Human-Centric: Make sure there’s always a person checking and responsible for decisions made with AI.
2. Know where you stand: Check how ready your company is to use AI, considering what your company can do and how risky your planned uses are. Adjust your approach to fit this and use what your company already has.
3. Earn Trust: Ensure stakeholders believe in your company and understand the public’s thoughts by developing abilities and spreading responsible AI values.
4. Employ Agility: Be ready to change quickly using a try-and-see approach, as rules and public feelings about AI change fast in different markets.
5. Act with Intention: Recognize that AI is changing and can be tricky. Be careful and always check to ensure things are working as expected to prevent unexpected issues.

1. Be Human-Centric: Transparency and Explainability

Adopt a people-focused approach by making AI systems clear and easy to understand. Creating AI with the ability to explain its decisions and operations is vital. Include features that track and monitor AI decisions, ensuring they’re unbiased and non-discriminatory.

When people interact with AI, they should know they’re doing so and always have the option to discuss issues with a natural person if they disagree with an AI-generated decision.

2. Know where you stand: Data Privacy and Robustness

Guarantee solid data privacy and robust infrastructure. With various foundational models and vendors available, picking the right one is crucial. Companies might choose everything from fully cloud-based to privately managed infrastructures.

The key is balancing the ease of using a single source against the risk of vendor lock-in, all while maintaining strict data security and privacy standards.

Constructing a tech framework that allows flexibility and adaptation to the ever-evolving AI ecosystem is pivotal, especially in ensuring customer and organizational data privacy and managing other risks in financial services.

3. Earn Trust: Regulation

Get ready for regulatory standards. Even though regulations regarding generative AI are still trying to catch up, companies should be proactive. This involves actively identifying, assessing, and managing risks and being forward-thinking in their approach towards governance and compliance.

This preparedness ensures they can swiftly adapt to new regulations, securing stakeholder trust by demonstrating a commitment to maintaining ethical and legal standards.

4. Employ Agility: Oversight and Disclosure

Guarantee consistent monitoring and clear communication both before and after AI deployment. With AI technology continuously advancing, maintaining oversight of applications to manage emerging risks post-launch is crucial.

Establishing clear guidelines for evaluating and testing models and utilizing tools like model cards that offer insights into the AI will facilitate the scalable and quantitative evaluation of foundational models before their roll-out.

5. Act with Intention: Maturity of AI through Governance

Consider the company’s organizational maturity and AI governance while deciding on applications. Beginning with low-risk uses when initially developing generative AI is wise.

As the company’s proficiency in managing AI responsibly extends, it can gradually move on to higher-risk applications. Start with applications used internally, then gradually expand to a limited external user base, and eventually, after refining feedback mechanisms, roll out applications to a broader audience.

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Conclusion: Navigating Forward

The path to responsible AI rests on a robust framework and a human-centric approach. Ensuring human oversight and feedback, particularly in training models like large language models (LLMs), is paramount to safeguard against biases and ensure alignment with human values and norms. Consequently, balancing leveraging AI’s potential and maintaining ethical, secure, and compliant operations is imperative.

The trajectory toward AI ubiquity in Finance and insurance is evident, yet we must steer the journey with an ethical compass. As companies race towards algorithmic innovations, the onus rests on ensuring that responsibility and moral alignment mark this journey and proactive mitigation of risks. Integrating these aspects will preserve and enhance customer trust and safeguard companies against reputational damage and regulatory sanctions, fostering a future where technology and ethics coalesce harmoniously.