Understanding Artificial Neural Networks and Their Applications

Do you find yourself hearing about neural networks everywhere yet wondering what they truly mean for your business? While terms like “neural networks in AI” sound complex, their real value lies in solving practical challenges, from customer engagement to operational efficiency. As a decision-maker, the question isn’t just “What is a neural network?” but rather how it can translate into measurable outcomes for your organization.

The journey of the AI neural network traces back to 1943, when Walter Pitts, a mathematician, and Warren McCulloch, a neurophysiologist, published a paper explaining how neurons might function. Along with Donald Hebb’s contributions as a psychologist, they laid the groundwork for building a simple neural network using electrical circuits. 

What began as a theory has now evolved into a field that powers deep learning. It is a branch of machine learning that enables systems to recognize faces, transcribe speech into text, and assist autonomous vehicles in avoiding real-time obstacles.

In this blog, you’ll explore artificial neural networks and discover their practical applications across industries like healthcare, finance, education, and logistics.

Key Takeaways

• Neural networks mimic the brain: ANNs use interconnected “neurons” arranged in layers (input, hidden, output) to process data, recognize patterns, and generate predictions.
• Learning happens in cycles: Forward propagation, loss calculation, backpropagation, and gradient descent work together to improve accuracy over time.
• Different networks for different problems: From CNNs for image recognition to RNNs for time-series forecasting and GANs for generating synthetic data, each type addresses unique SME challenges.
• Real SME impact: Retailers predict churn, fintechs detect fraud, logistics providers forecast delays, and healthcare firms classify medical images, turning data into measurable business outcomes.
• Challenges exist, but are solvable: Data dependency, computational costs, overfitting, and “black box” explainability issues make execution tricky, highlighting why the right partner is essential.

What are Artificial Neural Networks?

Artificial Neural Networks (ANNs), often simply referred to as neural networks, are computational systems inspired by the way the human brain functions. Just as your brain uses billions of neurons to transmit signals, process information, and make decisions, ANNs use interconnected artificial neurons to analyze data, identify patterns, and generate predictions.

Each neuron performs a simple mathematical operation, but when many neurons are connected in layers, the network becomes capable of solving complex problems. This layered structure allows ANNs to learn from experience.

They operate within the domain of machine learning (ML). By adjusting “weights” and “biases” between neurons during training, ANNs improve their accuracy over time in recognizing patterns or making predictions. That ability to continuously learn from data makes them essential for modern artificial intelligence applications.

Example: If you’re a retail business leader managing thousands of online transactions daily, an AI neural network can be trained to recognize unusual purchasing patterns. Instead of relying on manual checks, the network can flag potential fraud in real-time, reducing losses and protecting customer trust. This, in turn, impacts your bottom line.

Also Read: History and Development of Neural Networks in AI

Components of Artificial Neural Networks

At its foundation, every artificial neural network (ANN) is built from layers of interconnected nodes (neurons). Each layer has a specific role, and together they enable the system to learn from data and make predictions.

The Three Core Layers

1. Input Layer

• Receives raw data (e.g., text, numbers, images).
• Passes this information to the next layer for processing.

2. Hidden Layers

• Process the input data by performing calculations and extracting patterns.
• The more hidden layers there are, the deeper the network, hence enabling it to capture complex relationships.

3. Output Layer

• Produces the final decision or prediction.

Example: A mid-sized healthcare provider could use an ANN to process patient records (input), identify risk factors for chronic disease (hidden layers), and recommend early interventions (output). This improves patient outcomes and reduces long-term treatment costs.

Together, these layers form the backbone of every ANN. But layers alone don’t explain how the system actually improves with experience, which brings us to the learning process.

How Do Artificial Neural Networks Learn?

Artificial Neural Networks (ANNs) learn much like humans do, through repeated practice and feedback. Instead of memorizing, they refine their ability to recognize patterns by adjusting internal parameters until predictions become accurate. Let’s take a closer look.

The Learning Process

1. Forward Propagation: Data flows from the input layer through hidden layers to the output. The network then makes a prediction.

Example: Customer data (including purchase history, frequency of visits, and feedback) flows from the input layer through hidden layers to the output. The network predicts whether a customer is “likely to churn” or “likely to stay.”

2. Loss Calculation: The prediction is compared to the correct answer, and the difference (error) is calculated using a loss function.

Example: The prediction is compared with the actual outcome. If the model predicts “likely to churn,” but the customer remains active, the error is recorded using a loss function.

3. Backpropagation: The error is sent back through the network. Then, the system fine-tunes its connections (weights and biases) to reduce fumistakesrrors.

Example: The system learns to better connect signals, such as “declining cart size” or “longer gaps between visits,” with actual churn. Over time, these adjustments help the model align its predictions more closely with real customer behavior.

4. Gradient Descent: It is an optimization method that makes minor adjustments step by step. Think of it as nudging the model closer to the “sweet spot” where prediction errors are minimal. 

Example: Over time, the model becomes highly accurate in forecasting churn, allowing the SME to identify at-risk customers before they leave.

Why It Matters to SMEs? For a retail SME, this cycle translates directly into value. With accurate churn predictions, you can launch timely retention campaigns, like offering discounts or loyalty points to high-risk customers. Instead of reacting after losing revenue, you can proactively preserve customer relationships and stabilize growth.

Pro Tip: Start training with smaller, focused datasets before scaling to large ones. This reduces computation costs and helps SMEs validate the accuracy of their AI models early.

Also Read: Steps to Create and Develop Your Own Neural Network

As you can see, neural networks don’t “guess “; instead, they learn, improve, and adapt with every cycle of data. Once you understand how they learn, the next step is knowing that not all networks are built the same. 

Neural Networks: Major Types

Different neural networks cater to various business needs. Here are the most widely used ones and how they apply to SMEs:

1. Feedforward Neural Networks (FNNs)

• Also called Multi-Layer Perceptrons (MLPs).
• Information flows in one direction only; no loops or cycles.
• Best for basic classification and regression tasks.
• Limited for problems requiring memory or sequential context.
• Example: A healthcare SME using an FNN to classify patient test results as normal or abnormal, helping doctors prioritize cases faster.

2. Convolutional Neural Networks (CNNs)

• Designed for grid-like data such as images, videos, or speech spectrograms.
• Utilize convolutional filters to extract features such as edges, textures, and shapes.
• Ideal for image recognition, object detection, and speech analysis.
• It is the foundation of many computer vision systems (e.g., medical imaging, autonomous vehicles).
• Example: A healthcare SME using CNNs to analyze chest X-rays and detect pulmonary disease.

3. Recurrent Neural Networks (RNNs)

• Built for sequential data such as text, time-series, or IoT sensor data.
• Include feedback loops that provide memory of past inputs.
• Great for language modelling, speech recognition, and forecasting.
• Variants such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) are better suited for handling longer dependencies.
• Example: A logistics SME forecasting delivery delays using past traffic and weather data.

4. Radial Basis Function Networks (RBFNs)

• Two-layer networks:
  • First maps inputs to radial basis functions.
  • The second calculates the output.
• Effective for pattern recognition, interpolation, and clustering tasks.
• Useful when data clusters around reference points.
• Example: An energy SME utilizes RBFNs to identify abnormal power consumption patterns, enabling the prediction of equipment failures before breakdowns.

5. Generative Adversarial Networks (GANs)

• Consists of two networks:
  • The generator creates synthetic data.
  • Discriminator checks authenticity.
• Used for data generation, image synthesis, and simulations.
• Beneficial when real-world data is limited or costly to collect.
• Example: A travel SME utilizes GANs to generate realistic destination images for marketing campaigns, thereby reducing content production costs.

6. Modular Neural Networks (MNNs)

• Divide complex problems into smaller, independent modules, each handled by its own neural network.
• Modules process parts of the task in parallel, and their results are combined to produce the final output.
• Improves efficiency, scalability, and accuracy on multi-dimensional problems.
• Example: A fintech SME builds an MNN where one module predicts credit risk, another detects fraud, and a third analyzes customer churn. This combines outputs for more thoughtful decision-making.

Knowing the different types of neural networks is only half the picture. What truly matters for your business is how these networks can be applied to solve real-world problems. From improving customer retention to automating critical decisions, let’s look at where ANNs deliver tangible value.

Applications of Artificial Neural Networks

Artificial Neural Networks (ANNs) are no longer just research concepts. They are capable of powering real-world solutions that SMEs can use to stay competitive. By learning from data, ANNs uncover patterns, automate decision-making, and enable businesses to act more quickly with greater confidence.

Marketing & Sales

ANNs drive precision in customer engagement by processing millions of behavioral signals that humans can’t interpret manually.

• Product Recommendation Engines: Analyze browsing history, cart abandonment, and click-through rates to suggest hyper-relevant products in real-time.
• Customer Segmentation: Moving beyond demographic grouping, ANNs cluster customers based on nuanced behavioral patterns, such as purchase cycles and price sensitivity.
• Demand Forecasting: Predict seasonal spikes and regional demand fluctuations to optimize inventory levels and ensure optimal stock levels.

Example: A mid-sized online retailer uses ANNs to forecast holiday demand, ensuring warehouses are stocked correctly. This reduces overstocking costs while avoiding missed sales opportunities.

Healthcare

In healthcare, ANNs complement clinical expertise by detecting patterns invisible to the human eye. For instance:

• Medical Imaging: CNN-based models detect tumors or fractures from CT or MRI scans with accuracy comparable to that of radiologists.
• Treatment Simulation: Predict likely outcomes of chemotherapy, surgical procedures, or medication regimens using historical patient data.
• Risk Prediction: Identify high-risk patients for chronic diseases, such as diabetes or heart conditions, using electronic health records (EHR).

Example: A diagnostic SME applies an ANN trained on thousands of chest X-rays to detect pneumonia, allowing junior doctors to prioritize cases more effectively.

Finance

With financial transactions measured in billions per day, ANNs excel at spotting anomalies that no rule-based system can manage.

• Fraud Detection: RNNs detect suspicious sequences in transaction flows within milliseconds.
• Credit Scoring: Models combine unconventional data, such as spending behavior, repayment patterns, and even mobile usage, to provide more accurate loan approvals.
• Portfolio Optimization: Predictive models dynamically rebalance investments by analyzing historical price patterns and external economic data.

Example: A fintech SME integrates an ANN-driven fraud detection system into its payment gateway, reducing false positives while enhancing compliance reporting.

Also Read: Use AI to Transform Stock Investment Strategy

Customer Support

ANNs enhance both the speed and quality of customer support. Instead of static keyword-based bots, modern systems understand context and intent.

• Intelligent Chatbots: NLP models resolve FAQs instantly, freeing up agents for complex cases.
• Sentiment Detection: Classify incoming queries as positive, neutral, or negative, enabling managers to prioritize escalations effectively.
• Knowledge Base Optimization: Suggest the most relevant support articles dynamically.

Example: A SaaS SME deploys an ANN-powered chatbot that quickly resolves routine queries, such as billing or login issues, freeing support staff to focus on more complex customer problems.

Social Media & Content Platforms

Social media and content-heavy platforms rely heavily on ANNs to keep users engaged while maximizing ad revenue.

• Content Recommendation: Suggest content, friends, and groups tailored to user interaction patterns.
• Ad Targeting: Match user interests with advertiser goals for maximum ROI.
• Content Moderation: Detect and remove harmful posts in real time.

Example: A retail SME running targeted ad campaigns can witness higher ROI as ANN-powered ad systems deliver promotions to buyers most likely to convert.

Engineering & Autonomous Systems

ANNs are critical in industrial settings, where downtime directly translates into lost revenue. Here’s how:

• Predictive Maintenance: Identify early signals of equipment failure through vibration, temperature, and usage data.
• Fault Detection: Identify abnormal pressure or voltage fluctuations in real-time to prevent breakdowns from occurring.
• Autonomous Control: Enable systems, such as self-driving vehicles or drones, to process sensor data and make safe, real-time navigation decisions.

Example: A logistics SME utilizes ANN-based predictive maintenance on delivery trucks, resulting in a reduction in breakdown incidents and an extension of fleet life cycles.

Pro Tip: SMEs should train models on domain-specific datasets rather than generic ones. A medical SME, for instance, is likely to see better results from training on local patient records than on open, global datasets that may not accurately reflect its target population.

Wondering if AI/ML is overkill for your SME? With our AI and Machine Learning Consulting Services, we show where neural networks make sense, and where they don’t.

While ANNs deliver powerful results, they are not without drawbacks. For SMEs, these challenges can affect implementation, cost, and overall effectiveness.

Limitations of Artificial Neural Networks

For SMEs, the drawbacks of neural networks often manifest in very practical ways: data is challenging to source, training is resource-intensive, results can be slow to deploy, and decisions are not always explainable. The table below highlights the most common challenges.

Challenge	Impact on SMEs	Example
Data Dependency	It is challenging to source clean, high-volume datasets.	A healthcare SME with limited patient data may struggle to train reliable diagnostic models.
Computational Power	Requires expensive GPUs and infrastructure.	A fintech SME building fraud detection models may find GPU costs high compared to simpler alternatives.
Overfitting	Memorizes training data instead of learning general patterns.	A retail SME predicting seasonal demand may see models fail when faced with unusual events, such as supply chain disruptions.
Interpretability	Black-box decisions limit trust & compliance.	A bank SME denies a loan applicant but cannot clearly explain the model’s reasoning, raising regulatory concerns.
Training Time	Slower model deployment, delayed ROI.	A logistics SME may struggle to quickly roll out new demand forecasting models due to lengthy training cycles.

These challenges don’t mean you should avoid neural networks altogether. Instead, they emphasize the importance of finding the right partner. With the right expertise, SMEs can overcome these hurdles and turn complexity into real business value.

Why Codewave is the Right Partner for Neural Network Development

For many SMEs in healthcare, fintech, retail, and logistics, the real challenge is making sense of data. Valuable insights remain hidden in spreadsheets, transaction logs, and customer records, while inefficiencies, fraud risks, and missed opportunities pile up. 

Codewave addresses these pain points through advanced neural network development, combining AI/ML expertise with design thinking to transform raw data into clear, actionable strategies that drive measurable business outcomes.

Here’s how we can help you apply neural networks effectively:

• Custom Neural Network Models: We design deep learning systems tailored to your business, whether it’s detecting fraud in financial transactions, predicting equipment failures in logistics, or classifying medical images in healthcare.
• Data-Driven Predictive Analytics: By training models on historical datasets, we help SMEs forecast demand, optimize inventory, and anticipate customer churn. Overall, enables you to transform raw data into actionable strategies.
• Clustering & Pattern Recognition: Our solutions utilize frameworks like TensorFlow and Keras to segment customers, analyze behaviors, and identify hidden relationships across datasets, providing you with sharper market insights.
• Scalable Architectures: We ensure your models grow with your business. From simple FNNs for regression tasks to CNNs for image-heavy applications and RNNs for sequential data, our solutions scale as your data volume and complexity increase.

Explore our portfolio to see how we have developed neural network models across various industries, including fintech, healthcare, retail, logistics, and more.

Struggling with inaccurate demand forecasts, frequent equipment breakdowns, or fraud slipping past rule-based systems? 

Our AI/ML development services, powered by neural networks, help you reduce downtime, enhance forecasting accuracy, and identify risks in real-time. Schedule a consultation to get started.

FAQs

1. What is the role of the activation function in a neural network?

Activation functions introduce non-linearity, enabling networks to handle complex tasks. Examples include Sigmoid (binary classification), ReLU and Leaky ReLU (hidden layers, solving vanishing gradients), Tanh (outputs between –1 and 1), and Softmax (multi-class classification). Without them, networks could only solve simple problems.

2. What is used to train neural networks for AI applications?

Neural networks are trained using labeled data, where inputs are paired with correct outputs. Loss functions, including Mean Squared Error, Mean Absolute Error, and Cross-Entropy, measure prediction errors and guide weight adjustments to improve accuracy over time.

3. What is the future of neural networks?

Neural networks are advancing with transformer models for natural language tasks, large-scale networks with billions of parameters, and neurosymbolic AI that blends pattern recognition with logical reasoning. These offer SMEs more accurate, explainable, and business-ready AI solutions.

4. What language do you use for the development of neural networks?

We primarily use Python for developing neural networks, utilizing frameworks such as TensorFlow, Keras, and PyTorch. Depending on project needs, we also integrate R, Java, or C++ to build scalable, industry-specific AI/ML solutions for SMEs.

5. What is the most common neural network?

One of the most common neural networks is the Long Short-Term Memory (LSTM) network. LSTMs utilize gates (forget, input, and output) to determine which information to retain, discard, or pass along. This approach makes them highly effective for processing sequential data, such as language or time-series forecasting.