chatbots – The Data Community

Few industries have embraced AI as broadly—or as aggressively—as financial services. Banks, insurers, investment firms, and fintechs operate in data-rich, highly regulated environments where speed, accuracy, and trust matter. AI is increasingly the engine that helps them balance all three.

How AI Is Being Used Today

AI shows up across nearly every function in financial services:

Fraud Detection & Risk Monitoring
Machine learning models analyze transactions in real time to identify suspicious patterns, often catching fraud faster and more accurately than rule-based systems. PayPal utilizes AI-powered systems to detect fraud by comparing transactions with historical patterns, reducing financial losses. This is extremely critical in this time of rampant fraud. Financial Institutions also use AI to analyze real-time working capital and historical data to forecast financial performance and predict trends with greater accuracy.
Credit Scoring & Underwriting
AI evaluates borrower risk using far more signals than traditional credit scores, including transaction behavior and alternative data (where regulations allow). Upstart, an AI-based lending platform, uses non-traditional data to assess creditworthiness, approving loans quickly for customers who might otherwise be denied by conventional models.
Customer Service & Virtual Assistants
Chatbots and voice assistants handle balance inquiries, dispute tracking, loan status updates, and more—freeing human agents for complex cases. Bank of America’s Erica, a virtual assistant, assists customers with account information, bill payments, and personalized financial advice through chat or voice.
Algorithmic & Quantitative Trading
AI models analyze market signals, news sentiment, and historical trends to inform trading strategies and portfolio optimization. Goldman Sachs uses generative AI to optimize trading strategies and forecast market trends, gaining a competitive edge in dynamic markets.
Compliance & AML (Anti–Money Laundering)
AI tools assist in ensuring compliance with regulatory requirements by automating the monitoring transactions and reporting. This reduces the risk if non-compliance and associated penalties. HSBC utilizes AI to process compliance documents efficiently, ensuring adherence to evolving regulations and minimizing manual errors. AI also helps identify money laundering patterns, reduce false positives, and prioritize investigations.
Personalized Financial Advice
Robo-advisors and recommendation engines tailor savings, investment, and retirement strategies to individual customers. Wells Fargo’s predictive banking feature provides personalized prompts about future financial activities leading to improved user engagement.

Tools, Technologies, and Forms of AI

Financial services organizations typically use a layered AI stack:

Machine Learning & Deep Learning
Core to fraud detection, risk modeling, and forecasting.
Natural Language Processing (NLP)
Used to analyze customer communications, earnings reports, regulatory filings, and market news.
Generative AI & Large Language Models (LLMs)
Emerging use cases include advisor copilots, automated report generation, customer communication drafting, and internal knowledge search.
AI Platforms & Infrastructure
Cloud platforms like AWS, Azure, and GCP provide scalable ML services, while many firms also invest in proprietary, on-prem models for sensitive workloads.
Decision Intelligence & Optimization Models
AI combined with rules engines to support pricing, underwriting, and capital allocation decisions.
Blockchain and AI Integration
Blockchain and AI integration will redefine how financial transactions are managed, enhancing security, transparency, and efficiency. Blockchain technology ensures trust and accountability, while AI improves transaction validation and fraud detection. Together, these technologies will streamline cross-border payments, smart contracts, and digital identities, creating a more secure and efficient financial ecosystem.

Benefits Financial Institutions Are Seeing

Organizations that have successfully deployed AI are seeing tangible gains:

Reduced Fraud Losses and faster detection
Lower Operating Costs through automation of high-volume tasks and improved efficiencies
Improved Customer Experience with faster responses and personalization
Better Risk Management via more dynamic and data-driven models
Increased Revenue through smarter cross-sell, upsell, and pricing strategies

In short, AI helps firms move from reactive decision-making to proactive, predictive operations.

Pitfalls and Challenges

Despite the promise, AI in financial services comes with real risks:

Bias and Fairness Concerns
AI models can unintentionally reinforce historical bias in lending or underwriting decisions, creating regulatory and ethical challenges.
Model Explainability
Regulators and auditors often require clear explanations for decisions—something black-box models struggle to provide.
Data Quality and Silos
Poor data governance leads to unreliable models and failed AI initiatives.
Regulatory Risk
Financial institutions must ensure AI usage aligns with evolving regulations across regions.
Overhyped Projects
Some AI initiatives fail because they chase cutting-edge technology without clear business ownership or measurable outcomes.

Where AI Is Headed in Financial Services

Looking ahead, several trends are emerging:

AI as a Copilot, Not a Replacement
Advisors, underwriters, and analysts will increasingly work alongside AI systems that augment—not replace—human judgment.
More Explainable and Governed AI
Expect increased focus on transparency, auditability, and model governance.
Real-Time, Embedded Intelligence
AI will be embedded directly into workflows—credit decisions, claims processing, and trade execution—rather than sitting in separate tools.
Greater Use of Generative AI
From personalized financial guidance to internal knowledge assistants, GenAI will reshape how employees and customers interact with financial systems.

How Financial Services Companies Can Gain an Advantage

To stay ahead in this fast-changing landscape, organizations should:

Start with High-Impact Use Cases
Focus on areas like fraud, customer experience, or risk where ROI is clear.
Invest in Data Foundations
Clean, well-governed data is more valuable than the most advanced model.
Build AI Governance Early
Fairness, explainability, and compliance should be part of design—not afterthoughts.
Upskill the Workforce
AI-literate business leaders and domain experts are just as important as data scientists.
Blend Human and Machine Intelligence
The most successful systems pair AI recommendations with human oversight.

Final Thoughts

AI is no longer experimental in financial services—it’s essential infrastructure. Firms that treat AI as a strategic capability, grounded in strong data practices and responsible governance, will be best positioned to innovate, compete, and earn trust in an increasingly intelligent financial ecosystem.

Are you using AI in the financial services industry? Share how and what you have learned from your journey.

This article is a part of an “AI in …” series that shares information about AI in various industries and business functions. Be on the lookout for future (and past) articles in the series.

Other “AI in …” articles in the series:

AI in the Hospitality Industry: Transforming Guest Experiences and Operations

AI in Gaming: How Artificial Intelligence is Powering Game Production and Player Experience

AI in Healthcare: Transforming Patient Care and Clinical Operations

Thanks for reading and good luck on your data journey!

What is a Salesforce Einstein Bot?

According to Salesforce a bot is “a computer program which conducts a conversation via auditory or textual methods.”.

So, before we get more into what a bot is let’s first look at the platform they are created on, Salesforce’s Einstein Analytics.

Salesforce’s Einstein Analytics provides impressive mechanisms that assist organizations and their users of the Salesforce platform to connect, communicate & interpret customer needs. By implementing elements of artificial intelligence, data mining and predictive analytics Salesforce users can get deeper insights into their customers data and begin to build an improved base of knowledge related to their business. With an underlying engine tuned for performance and a presentation layer which can display key details or high level metrics on dashboards Einstein Analytics is the next step in reporting on the health of your sales pipeline, exposing opportunities and providing suggestions to help guide you in identifying & visualizing growth which aligns to your business.

Now, back to bots …

Basically, a bot is a means to facilitate communication between humans and computers with either voice or text and subsequently executing an action tied to the input provided. Bots can learn over time to interact with humans by leveraging Salesforce’s Einstein Analytics platform and your data which resides in Salesforce and respond using Natural Language Processing. According to Wikipedia, “Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.”

Why are Salesforce Einstein Bots important?

Organizations are creating and implementing bots at an ever-increasing rate. By creating and implementing a bot an organization can begin to get a handle on support cases resolving many of them very quickly and for some scenarios eliminating the need to open one at all.

Of course, a bot isn’t something that is intended to supplant interaction with a human. However, they can be leveraged to provide a decision path for customers and route customer’s requests quickly based on their general needs while providing a positive initial reception which can augment your current customer service model.

Not only can bots improve productivity of agents by freeing them from having to spend time addressing some of the simpler, frequent requests but can now allow them to focus on more time consuming, complex issues.

Bots in a sense can also be considered another channel for content. However, instead of thinking of new ways to formulate questions from scratch organizations should try to marry current content to bot questions. Reusing content is good but it should rely on content that is based on existing knowledge. This reuse of inhouse documentation & materials will ultimately bring development costs down leading toward a more uniform experience with a higher degree of excellence for the interaction.

How to configure the platform for Salesforce Einstein Bots?

Before you jump in and start creating bots you would be best served by allocating time to plan your bot and consider how it will interact with your customers.

Collaborating and soliciting feedback from agents regarding the issues they experience with customers that are potential areas a bot could address is a good start.

Think about the bot’s persona, what its name should be and how you would like it to convey & reiterate a consistent image of the company overall.

Decisions related to which channels to use, ways in which customers can enter their questions, which licenses are required, which profile to use, whether to provide a menu, what is not in scope for the bot, … etc. should all be worked out in advance of bot development.

At what point does a human need to take over from the bot’s interaction with the customer, if at all?

In Salesforce you will need a Service Cloud license and a Chat or Messaging license. Once that is obtained you will need to turn on Lightning Experience. There is a guided setup flow for Chat you will need to run through. If your organization has Knowledge articles you want to make available to customers through the bot that will need to be enabled also. In your Salesforce Org if you go to Setup and type Einstein Bots in the quick select area it will return Einstein Bots you can click on. Then under Settings there is a toggle to enable Einstein Bots.

When ready make an Embedded Chat button available on your published Salesforce site or community site for your customers to interact with. A Salesforce community site is preferred.

Check out the https://trailhead.salesforce.com/en/home free training to find out more about how to create bots within Salesforce.

Things to consider when maintaining Salesforce Einstein Bots.

Salesforce documentation indicates that the following items also be considered when planning bot creation:

Chat and Messaging licenses support different channels (such as SMS or Facebook Messenger) and might have different requirements.
Each org is provided 25 Einstein Bots conversations per month for each user with an active subscription.
To make full use of the Einstein Bots Performance page, obtain the Service Analytics App.