Nature is without a doubt the finest engineer and developer of all. As a result, scientists frequently emulate natural items' principles and structures in their devices.

Neural networks are one of those instances. They may not only follow given algorithms and formulae but rather operate based on past experience.

Neural networks are at the forefront of cutting-edge technology these days. Statistics show that the use of artificial neural networks in business has grown an astounding 270% over the last few years.

With the vast array of information available on neural networks, there is unfortunately very limited information on how you can use NNs in your business.

That’s where we come in. This article will provide you with all the information you would need to know regarding adopting neural networks in business. If you want your company to be—and stay—competitive, this may be just what you need.

What are artificial neural networks?

what an artificial neuron looks like
Anatomy of an artificial neuron

An artificial neural network (ANN), often simply referred to as a neural network (NN), is an attempt to replicate the brain's network of neurons so that the computer may learn and make judgments in a human-like manner. Simply put, they are computational models, or what we know as algorithms.

These NNs are made by programming regular computers to act as though they are linked to nerve cells. Built of numerous, interconnected, and layered simple processing elements, they mimic certain aspects of the brain's physical structure and information processing.

An ANN, like its biological counterpart, has the following attributes:

  1. Each processing element (essentially, a neuron) receives information from other elements.
  2. The values and inputs are weighted and combined.
  3. The output is then transformed via a transfer function into the desired result.

Neural networks are basically an attempt to model our own thought process, imitate the way humans think, and discover patterns within a dataset for us to make predictions or discover hidden value within the data.

With an idea of what neural networks are, let’s take a closer look at how they work.

Neural networks and deep learning: how they work

Neural networks are created by an arrangement of interconnected nodes. Nodes are typically arranged in layers, with the input layer at the very bottom and the output layer at the top. The neural network is usually multi-layered.

artificial neural network architecture map
Architecture of an artificial neural network

With multi-layer neural networks, developers arrange processors in layers that work in parallel to do their job:

  • Input Layer: This layer is comparable to the dendrites in a human brain's neural network.
  • Hidden Layer: This layer is similar to the cell body as it sits between the input and output layers, like the synaptic connections in the brain. The hidden layer is where artificial neurons process a set of inputs based on synaptic weight, which is the amplitude or strength of a connection between nodes.
  • Output Layer: The transfer function that's applied to this weighted data creates the output received in the output layer. This is what you and your clients will see.

In the same manner that a toddler learns by touching, tasting, and smelling its surroundings, this AI system will learn with each input experience. The algorithm will modify the internal connections until it figures out how to produce the desired output within a certain level of accuracy.

When the algorithm has learned, more inputs can be fed into it, where it can be used to solve several other problems.

But where does Deep Learning come in?

Deep Learning (DL) is a more rigorous form of machine learning. Similar to the single hidden layer in the ANN, DL employs numerous hidden layers instead of just one.

Deep learning neural networks are not only more complicated, but they also offer the potential that the algorithms will take off and start learning on their own. The algorithms used in today's technology, whether it's basic machine learning, NN, or DL, still rely on external inputs such as people.

Next, it’s important to touch on the reasons why businesses are utilizing neural networks.

Why do we use neural networks?

Neural networks' capacity to mimic human-like behaviors and operate on a wide range of problems makes them ideal for today's big data-based systems.

Since NNs have the ability to make sense of ambiguous, conflicting, or incomplete information (fuzzy logic), they may employ controlled procedures when no precise models are accessible.

According to Statista, global data volumes reached almost 100,000 petabytes (one million gigabytes) each month in 2017 and is expected to reach almost 181 zettabytes (181 trillion gigabytes) by 2025.

With organizations, people, and devices generating enormous quantities of data, all of this data can now be used to extract meaning via neural networks (NN).

The capabilities of neural networks include:

Adaptive learning

Neural networks, like humans, represent non-linear and complicated interactions, as well as building on previous knowledge. Software, for example, uses adaptive learning to educate children in math and language arts.


The ability to group and classify massive amounts of data makes neural networks ideal for dealing with the complex visual issues presented by medical imaging. These images are often difficult for humans to decipher, whereas a neural network can learn to group the different body parts automatically.

Pattern recognition

Neural networks excel at the challenge of identifying faces by learning to identify patterns in facial features and expressions. This talent makes them perfect for applications such as security systems that must analyze live video footage.

Real-time operation

Neural networks can, on occasion, provide real-time responses, as can be seen with self-driving cars and drone navigation.

Big Data analysis

Neural networks can provide valuable assistance when analyzing large datasets. The algorithms used by neural networks help to reveal relevant patterns and relationships between variables, which may not be obvious using other data-analysis tools.


The prediction ability of NNs based on models allows the application of a vast range of business models, including, but not limited to, weather and traffic forecasts.

Fault tolerance

Neural networks are fault-tolerant, meaning they can continue to perform even if one or more nodes fail. Neural networks can fill in the gaps when significant portions of a network are lost or missing.

This ability makes them useful for mission-critical systems that must work around the clock without hiccups, for example in space exploration, where electronic equipment failure is almost certain.

Now, you might be wondering how Neural Networks are already being applied across industries, let’s discuss this next.

What real-life business applications do neural networks have?

Neural networks are the new, smarter way of identifying hidden patterns within your data set and making predictions based on them. As mentioned previously, neural networks have a vast array of business applications and have been helping businesses in automating tasks that were earlier being done manually.

Let’s take a closer look at real-life examples of NN business applications:

Marketing and eCommerce

The most recent development in data science is the usage of big data to train neural networks. This technology has been around for decades, however, it is the more recent rise of Big Data that makes it highly useful for marketing.

Marketers can use these tools to find and reach customers more effectively, which benefits them in the following ways:

These tools, provided enough data, can now deliver more precise insights and predictions, allowing marketers to make better strategic decisions. That being said, this technology also enables specialists to be less reactive by allowing them to better understand what other marketers need to target their ideal customers.

ANNs are most often used in the area of predictive analytics. The neural networks in business may be used to assist marketers to make predictions about the campaign's results by recognizing patterns from past marketing efforts.

An example of this is the personalization of product recommendations on eCommerce sites like Amazon. The system analyzes the user’s past behavior, purchases, what similar products they’ve viewed for more fitting recommendations, boosting the basket size, etc., to provide a more comprehensive marketing strategy.

Retail and Sales

Following on from the above business application of ANN, retail and sales are finding these systems and algorithms extremely valuable, too.

By using ANNs, retail and sales-driven companies are able to engage in:

  • Demand forecasting: This forecasting helps to identify when a product or service will be required by consumers, how to provide continuous product availability and on-time deliveries, etc.
  • Sales forecasting: This forecasting type helps to identify when and what a customer is most likely to buy. As a result, businesses can identify and understand the factors that contribute to higher sales in retail stores, as well as estimate future sales numbers.

An ANN-powered system is able to calculate the quantities of inventory that retailers should have. As a result, they can also work on enhancing their profits.

Finance and Banking

Neural networks can also be applied to automate processes in banking and finance.

Among other things, NNs can be used to:

  • Predict currencies
  • Business failure predictions
  • Assessments of debt risk
  • Approval of credit
  • Approval of mortgages
  • Fraud detection

A real-life example of this is the use of artificial neural networks to detect fraud used by Citibank. They have set up a neural network that aims to detect fraudulent transactions on credit cards. This neural network was trained on a large database containing millions of transactions by consumers.

Uses of neural networks in financial security


Neural networks are also being used for security purposes.

They can be used to:

  • Detect fraud
  • Malware and viruses can be detected and prevented
  • Prioritize alerts to send to the relevant people
  • Spam detection
  • Content moderation
  • Detecting DDoS attacks

An example of how neural networks are applied in business security is their use in detecting DDoS attacks. Detecting Distributed Denial of Service (DDoS) attacks is possible using neural networks. The system can watch out for patterns such as a large number of requests coming from a single IP address, or many requests at once from random IP addresses.

Another is ICSP Neural from Symantec, which protects against cyber assaults by detecting and exploiting viruses and zero-day flaws on USB devices.


Another industry making use of the advantages provided by the NNs is Insurance.  Insurance companies make use of neural networks to forecast future loss ratios and adjust premiums. This, in turn, increases their profit margin.

Some uses of neural networks in insurance include:

  • Forecast future loss ratios and premiums
  • Adjust future premiums
  • Detect fraudulent claims

A real-life example of how neural networks are being used in insurance is provided by Allstate. They are using neural networks to pick out "accident-prone" drivers and give them an appropriate rate.


Neural networks are being used in logistics to help with everything from packaging to shipping.

Some uses in logistics include:

  • Neural networks can be used to help package products
  • Neural networks can be used in routing to help determine the best route for a truck driver
  • Neural networks are used to identify defects in the production line.
  • Neural networks are being used in dispatching to help with the packaging of items for transportation
  • Neural networks are being used to balance out an assembly line by assigning jobs to workers based on their skill sets.

An example of how neural networks are being applied in logistics is Wise Systems. This is an autonomous system that lets a user plan and monitor routes, and customize real-time shipping routes using predictive functionalities.

Key takeaways

As a subset of Machine Learning, artificial neural networks are central to deep learning. They work similarly to biological neural networks by connecting different types of neurons and data. These networks make highly accurate predictions through data training.

Neural networks may be quite beneficial for a variety of organizations, as we've mentioned previously. And if you need any help getting started on taking your business to the next level with NNs, contact us and one of our team members will be happy to walk you through it!

Now that you're here

Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.‍If you liked this blog post, you'll love Levity.

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Now that you're here

Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.‍If you liked this blog post, you'll love Levity.

Sign up

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