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Embracing the Mundane in AI — the Need for Specialised AI in Financial Services

Why we need more specialised AI which is more accurate for Financial Services tasks, not bigger and more capable headline AI systems.

In the rapidly-evolving landscape of artificial intelligence (AI), it’s easy to get caught up in the whirlwind of excitement surrounding the latest advancements. The AI community is again buzzing with discussions and papers devoted often to building bigger and more complex language models (LLMs) and broad API toolsets. It paints a picture of a future where AI serves as a personal assistant, ready to tackle any challenge alongside us. This is useful as a personal co-pilot and I would like one! However, this pursuit of a broad, capable AI agent misses what I think businesses need, especially within the realms of insurance, pension administration, and banking.

Whether it’s answering customer inquiries about a product, guiding someone through a process, or providing the financial guidance, the need is to solve that narrow mundane, yet crucial, task. This is at the core of customer management and financial guidance.

The truth is, that ‘big’ AI systems are not well suited to these applications out of the box. For a start, these large, all-encompassing systems are expensive and often slow to run. More problematic though, is that these big and broadly capable AI agents are often very flaky on real-world tasks that the business actually cares about. Accuracy is often low and is always rather uncertain unless you have done a huge amount of work to pin down and test across huge example datasets. No tech or project person wants to do this testing.

A somewhat better approach is techniques such as RAG (Retrieval Augmented Generation) for AI in which the AI uses a compendium of content to help craft its answers. This definitely works better the firms that are running POCs are mostly using these techniques. But in our experience, RAG is useful but not sufficient. There is a temptation to add more content to the compendium and this tends to just make the answers less reliable. How you curate the compendium of content makes a huge difference in the quality and accuracy of answers. So we cannot get away from the fact that subject matter expertise and content knowledge matter when building these systems.

Our approach goes a step further than this: a network of specialist AI agents. Our platform is built on the principle that specialised components, each focusing on a narrow task, can achieve significantly higher accuracy than their generalist counterparts. These expert agents can handle specific inquiries or do specific jobs with precision. We can link them together seamlessly to create more comprehensive customer journeys.

Opting for a network of specialised AI agents approach offers several advantages. Most importantly, we can test more comprehensively and deliver a significantly higher accuracy on tasks that businesses care about. Specialised agents can also be much more transparent, moving away from the “black box” nature of larger AI systems. And this network of specialists means you can build or buy components and plug-in specialist AI agents into your broader agent pipeline where you don’t have the internal skills to build or maintain that piece.

The trade-off with this network of specialists approach is that generality is lost. The AI system is now much narrower in capability, but much deeper on accuracy within that capability space. This type of AI will not be able to answer anything and everything, as you may wish for a personal co-pilot. We believe this is actually good thing for real use cases where businesses want to plug in AI to help customers through specific journeys. To draw a parallel, in a call centre, the operations manager does not hire a team of Einsteins to staff the team. Such a choice would be both overkill and misaligned with the job to be done.

The main takeaway from all of this is to say that I believe that the future of useful AI in financial services is not about chasing the latest developments or models. Rather it is about focusing on the mundane tasks and doing the boring nitty-gritty work of creating specialist components that deliver a really high accuracy that businesses can trust.

Many in the AI space, in particular in research, may have quite different view on this.

Find out more — https://engagesmarter.ai

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Embracing the Mundane in AI — the need for Specialised AI in Financial Services was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Machine learning has revolutionized various fields by enabling computers to learn from data and make accurate predictions or classifications. Two prominent types of models used in machine learning are generative models and discriminative models.

Generative models focus on capturing the underlying patterns of data to generate new examples that resemble the original dataset, while discriminative models concentrate on classifying or discriminating between different categories based on input features.

In this article, we will delve into the concepts of generative and discriminative models, exploring their definitions, working principles, and applications.

By understanding the differences and applications of these models, you will gain valuable insights into how they can be utilized in various domains, including anomaly detection, data augmentation, image generation, text generation, and more.

So, let’s dive into the world of generative and discriminative models.

Generative Models

Generative models are machine learning models that focus on building statistical models of the underlying distribution of a dataset.

Their aim is to learn patterns from the data and generate new samples with similar characteristics. These models excel at creating realistic new examples by capturing the underlying patterns present in the dataset.

Examples of Generative Models

Generative models encompass various algorithms that capture patterns in data to generate realistic new examples. Let’s explore some commonly used generative models:

1. Gaussian Mixture Models (GMMs): GMMs represent the density function of a dataset as a mixture of several Gaussian distributions. They can identify the distribution of continuous data and mimic it.
2. Hidden Markov Models (HMMs): HMMs are probabilistic frameworks used to model time series data. They predict the next observation in a sequence based on the hidden states of the process.
3. Variational Autoencoders (VAEs): VAEs are neural networks that learn the underlying distribution of the input data and generate new data points. They map input data to a latent space, which is used to reconstruct the input or generate new data points.
4. Generative Adversarial Networks (GANs): GANs employ two neural networks: a generator that creates data and a discriminator that checks if it’s real. The generator refines its output to fool the discriminator.

How Generative Models Work

Generative models aim to learn the underlying probability distribution of a given dataset.

They seek to understand the patterns and structures inherent in the data to generate new samples that capture the same distribution.

The fundamental idea behind generative models is to create a model that can statistically generate new data points resembling the original dataset.

To achieve this, generative models utilize techniques such as density estimation, latent variable modeling, and probabilistic graphical models.

These techniques enable the model to capture the complex relationships between variables and generate new data points based on the learned distribution.

Applications of Generative Models

Generative models have diverse applications where the ability to generate new data is valuable. Some areas where generative models excel include:

Image Generation

Generative models can generate realistic images, such as creating new faces or producing artwork.

Text Generation

Generative models can generate new text that resembles human-written content. This is useful in natural language processing tasks.

Anomaly Detection

Generative models can detect anomalies in data by identifying samples that deviate significantly from the learned distribution.

Data Augmentation

Generative models can generate additional training examples, improving the performance of other machine learning models.

By applying generative models in these areas, researchers and practitioners can unlock new possibilities in various domains, including computer vision, natural language processing, and data analysis.

Discriminative Models

In contrast to generative models, discriminative models focus on learning the direct mapping between input variables and output labels without explicitly modeling the underlying probability distribution of the data.

These models excel at classifying or discriminating between classes or categories based on the available input features.

Examples of Discriminative Models

Discriminative models encompass a range of algorithms that excel in diverse tasks such as classification and sequence analysis. Let’s explore some commonly used discriminative models:

1. Logistic Regression: Logistic regression estimates the likelihood of a binary result based on input features. It is frequently used in tasks involving categorization.
2. Support Vector Machines (SVM): SVM finds an optimal hyperplane to separate different classes in high-dimensional spaces. It is effective for both binary and multi-class classification.
3. Artificial Neural Networks (ANN): ANNs are flexible discriminative models composed of interconnected layers of artificial neurons. They can learn complex mappings between input and output variables.
4. Convolutional Neural Networks (CNN): CNNs are specialized deep learning models commonly used for image classification tasks. They extract hierarchical features from input images for discriminative classification.
5. Recurrent Neural Networks (RNN): RNNs are discriminative models that process sequential data, such as time series or natural language data. They capture temporal dependencies and are widely used in tasks like language translation and speech recognition.

How Discriminative Models Work

Discriminative models learn the direct mapping between input variables and output labels.

Unlike generative models, which model the joint distribution of inputs and outputs, discriminative models focus on modeling the conditional probability of the output given the input.

Underlying Principles

Discriminative models aim to find the decision boundary that separates different classes or categories in the input space.

By observing the input features and their corresponding labels, the models estimate the probability of a specific output label given the input.

They optimize the decision boundary based on training data by using various mathematical techniques and algorithms to decrease the error between expected and actual outputs.

Training and Inference Process

Training discriminative models involve feeding the model-labeled training data.

The model iteratively updates its parameters to minimize the difference between predicted and true output labels. Optimization algorithms, such as gradient descent, are commonly employed in this process.

Once trained, the model can be used for inference by taking unseen or test data as input and calculating the probability of each possible output label. The label with the highest probability is assigned as the predicted output.

Applications of Discriminative Models

Discriminative models find applications across various domains. Some key areas where they excel include:

Natural Language Processing

Discriminative models are employed in tasks like text classification and sentiment analysis to predict the category or sentiment of text. They assist in email spam detection, article classification, and customer feedback analysis.

Suggested Reading: Natural Language Processing

Computer Vision

Discriminative models, especially CNNs, are extensively used in object identification, picture segmentation, and image classification. They can distinguish people, identify items in photos, and find irregularities in medical imaging.

Speech Recognition

Discriminative models, particularly RNNs, are utilized in converting spoken words into written text. This enables voice-controlled applications and transcription services.

Financial Analysis

Discriminative models can be applied in financial analysis tasks, such as fraud detection, stock market prediction, and credit risk assessment.

By leveraging discriminative models in these domains, professionals can make more accurate predictions, gain insights from data, and drive better decision-making processes.

Conclusion

In conclusion, generative and discriminative models are two distinct approaches to machine learning.

Generative models focus on generating new examples by capturing the underlying patterns in the data, while discriminative models concentrate on classifying or discriminating between different classes based on input features.

Both types of models have wide-ranging applications across various domains and can be utilized to solve complex problems.

By understanding the principles, examples, and applications of generative and discriminative models, you can unlock the potential of these powerful machine learning techniques.

Frequently Asked Questions (FAQs)

What is the main difference between generative and discriminative models?

In generative models, the focus is on capturing patterns and creating new data, while discriminative models aim to classify or discriminate between different categories based on input features.

Can you provide examples of generative models?

Some examples of generative models include Gaussian Mixture Models, Hidden Markov Models, Variational Autoencoders, and Generative Adversarial Networks.

How do generative models work?

Generative models learn the underlying distribution of a dataset to generate new data points. They use techniques like density estimation, latent variable modeling, and probabilistic graphical models.

What are the applications of generative models?

Generative models are used in image generation, text generation, anomaly detection, and data augmentation, among other areas where the ability to generate new data is valuable.

What are some popular discriminative models?

Some popular discriminative models include Logistic Regression, Support Vector Machines, Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks.

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Understanding Generative and Discriminative Models was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Hi there,

I am creating the next round of AI content and would like to focus on the areas you are most interested in.

Right now I am focusing on 5 primary AI Topics.

Here is what each AI Topic Includes:

1. Insight Article

• Future & AI & LLMs: Where we are and where we are going within AI
• Big Picture Perspective: Impact on Media, Internet, Culture, Businesses, Ethics, AI governance, privacy, security, etc…

2. Tutorials & Deep Dives

• Deep Dives: What things are and how they work (eg: Training Models, Knowledge Bases, RAG, CUX, Flows/NLU, Challenges: Bias, Hallucinations, etc)
• Tutorials: How to work with them (eg: Building a Bot; Content Writing for LLMs)

3. Experiments

• AI Experiments: Building AI Agents, LLMs, RAG, Prompt Chaining, etc.
• Business: AI to for SEO/Lead Gen/Sales/Customer Service, Productizing AI

4. Roundups:

• Quick List: Reviews of topics, curated lists of insights or tools, and periodic statistics that offer snapshots of the AI landscape.

5. Tools & Resources

• List of Tools, Templates, and other Resources

Please let me know which topic interest you most in the 10 sescond poll below.

Also, feel free to suggest any other topics in the comments.

Cheers

Stefan

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What type of AI & Chatbot Content are you Interested in? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.