Generative AI is a powerful tool for the financial services and investment industry.
The ability to make faster, more informed decisions and drive operational efficiencies is needed in financial services.
In 2020 alone, over 726 billion digital payments were made across the globe. 25% of those transactions were reviewed manually, causing delays, errors, and fraud.
The benefits go far beyond decision-making.
Generative AI has real-world use cases in financial services, including:
- Chatbots
- Fraud detection
- Assisting brokers in choosing investments
- Personalized calculation of creditworthiness
- Peer-to-peer payment among digital wallets
- Assisting consumers in lowering their loans or debts
- Facial or voice recognition for biometric authentication
The applications are limitless — which raises a crucial question:
How do you ensure trust in Generative AI’s implementation in the financial services industry?
We’ll explore that in this article.
The Limitations of AI in Financial Services
While Generative AI is a powerful tool, it does have limitations, especially in the financial services industry.
Namely, the lack of transparency and visibility into the AI model.
- Why did the model choose the response it gave?
- Were there alternative responses the model could have given?
- How certain is the model in its answer?
- Does the model pull from any sensitive or inaccurate data?
- How was it developed, trained, monitored, or tuned?
- Does it have any biases influenced by pre-existing societal prejudices?
Any of these limitations can impact the trustworthiness of a model.
The Risks of Generative AI
The risks of Generative AI include:
- Data Source: Data used in model training and updates could include copyright materials, information from countries or organizations with alternative views, classified information, PHI (Protected Health Information), CUI (Controlled Unclassified Information), or other sensitive data sources that are not intended for inclusion in Generative AI models.
- Model Governance: Generative AI models provided by commercial organizations, including OpenAI, do not provide insights into how models are developed, trained, monitored, or tuned. Without an understanding of these processes, the goals and outputs of the model can be misinterpreted by end-users.
- Model Transparency: Generative AI models evaluate numerous data points before providing an output to the end user. In many cases, the model must evaluate multiple options before providing an output to the user. Commercial offerings do not provide visibility to model uncertainty, provide an explanation, context around the response, or alternative responses that the model could have provided.
- Model Biases: Generative AI systems might exhibit biases influenced by social and demographic variations from their training datasets and algorithmic structures. If not properly addressed, these models have the potential to absorb and magnify pre-existing societal prejudices related to race, gender, age, and ethnicity, among other factors, embedded in source data.
Generative AI in Transaction Monitoring
Let’s look at a specific example to see how these limitations play out: transaction monitoring.
Imagine you work for a bank incorporating Generative AI to monitor transactions.
When the AI model is being developed, it is trained with historical anonymized and aggregated data, allowing it to predict events and score transactions based on historical patterns.
Once the model goes into production, it receives millions of data points that interact in billions of ways, producing outputs faster than any team of humans could.
That AI model can help reduce ‘noise’ in data collection, leading to fewer false positives and helping transaction monitors recognize risky transactions—a huge benefit!
And this is where the risk comes in.
The AI model may generate these outputs in a closed environment, understood only by the team that originally built the model.
Not only that, but the data set may have introduced an unintended and prejudiced bias into the model, resulting in false positives that occur far more often for specific ethnic groups.
That’s why transparency in the model is so important.
What is Explainable AI?
Explainable AI gives human users transparency and visibility into all aspects of the AI model. This allows them to understand and trust interactions with AI models, especially the model outputs.
Here is a simplistic look at how it works:
- Reason codes are assigned to outputs visible to the model’s users
- Users can review those codes to both explain and verify outcomes
Going back to our example:
An account manager or fraud investigator suspects several outputs exhibit similar bias.
They can review the reason codes to see if a bias exists. Developers can then alter the model to remove the bias, helping to ensure a similar output doesn’t occur again.
The Power of Explainable AI in Financial Services
Explainable AI brings significant benefits to the financial services industry.
Visibility into the model and understanding why it generates a specific output helps facilitate trust, accountability, and compliance.
Here are a few ways Explainable AI impacts the financial services industry.
Risk Assessment and Mitigation
Explainable AI provides transparency into the factors and variables considered in risk models, allowing users to understand and validate risk assessments.
Instead of trusting the output, users gain insights into the analyzed data and why the model chose that specific output.
Going back to our example:
The bank could use Explainable AI to assess creditworthiness.
After analyzing various data points (credit history, income, demographic information, credit score, and more), the Explainable AI model can explain the credit decisions it outputs.
This would help ensure fairness and reduce the risk of discriminatory practices in lending.
Compliance & Regulatory Requirements
Explainable AI also helps financial institutions comply with regulatory frameworks by providing auditable and transparent decision-making processes.
These processes are documented — making it easy to understand and justify decisions made by the AI model.
Going back to our example:
The bank can use Explainable AI to analyze vast amounts of financial data, flag suspicious transactions, and provide explanations for detecting fraudulent activities.
This transparency helps compliance officers ensure regulatory guidelines are adhered to.
Portfolio Management and Investment Decisions
Explainable AI can assist portfolio managers and investors in asset allocation, portfolio optimization, and investment strategy creation.
It does this by:
- Analyzing historical market data
- Identifying patterns
- Providing explanations for recommendations
That last point is key.
Understanding the rationale behind the AI model outputs, portfolio managers and investors can evaluate the risks and benefits they are comfortable with and make well-informed decisions.
Customer Trust & Education
Explainable AI helps financial institutions build trust with their customers.
Take Robo-Advisory Platforms, for instance.
Most of the largest investment firms provide some form of robo-advisor:
- Betterment
- SoFi Automated Investing
- Vanguard Digital Advisor
- Wealthfront
- Schwab Intelligent Portfolios
- Fidelity Go
- Robinhood
Now imagine if those robo-advisors provided explanations for their investment recommendations.
Customers would be able to understand why those recommendations were suggested, giving them a reason to trust those recommendations.
They would also learn more about making financial decisions and how those choices can align with their goals.
Preventing Bias in Finance
Explainable AI can also help prevent bias and prejudice in financial decisions.
Generative AI models are prone to bias due to their limited data set and ability to absorb and magnify pre-existing societal prejudices embedded in source data.
Without Explainable AI, the model may generate outputs that discriminate against applicants based on protected characteristics related to race, gender, age, and ethnicity.
With Explainable AI, account managers, fraud investigators, portfolio managers, and the like can review the data that led to a decision — helping to ensure that the model did not introduce bias into its output.
Smart Money Moves with Explainable AI
Explainable AI is the smart way for financial institutions to embrace AI models.
By removing the black box of Generative AI and giving users transparency into the data and why the model chose its output, users can bypass the risks and confidently use AI models in the financial services industry.
At Ulap, we develop, train, monitor, and tune models and integrateExplainable AI concepts for the financial services industry.
See how we can bring your AI model to life.
