ben brown

January 6, 2023

Business

5 Industries Using Embedded Financial services in 2023

ben brown

January 6, 2023

Business

Embedded financial services, commonly referred to as "banking-as-a-service," is a business model where financial services are integrated into the products and services of non-financial companies. As we are now in 2023, we can expect this model to become even more prevalent as companies are consistently seeking ways to add value for their customers in addition to streamlining their operations.

That’s a lot to take in from the jump but it covers the basic idea of how BaaS fits into the generic landscape of businesses. This is yet another versatile piece of technology that can transform an organization's operations from the ground up.

2 Leading Factors Behind the Adoption

Demand for convenience: The first key driver behind the growth of embedded financial services is the increasing demand for convenience. Customers today expect to be able to access financial services on their own terms. To accommodate this, companies must offer flexible, high-performance experiences that allow users to manage their finances from anywhere, at any time. By integrating financial services directly into their products/services, companies can provide a one-stop-shop experience.

Data privacy and security: As data breaches become more common and customers become more concerned about the handling of their personal information, companies that can ensure and offer secure financial services are likely to be more attractive to consumers. By embedding financial services into their products and systems, companies can better control and protect their customers' data, which can give them greater confidence in using their services.

How Companies Are Capitalizing

There are several different ways that companies can use embedded financial services to benefit their customers as well as their operations. Every industry is searching for the best way to apply this. With that said, here are 5 that have found some success in doing so:

E-commerce: Many e-commerce companies, such as Shopify and Amazon, offer embedded financial services like financing options and payment processing. This allows customers to make purchases and pay for them over time, rather than having to pay upfront, which can increase sales and customer satisfaction (BNPL).
Travel: Companies in the travel industry, such as Expedia and Airbnb, often use embedded financial services to offer a range of payment options, one of which includes the ability to pay in different currencies. This can be especially useful for travellers who are visiting a country for the first time and aren’t familiar with the local currency.
Retail: Retail companies, such as Target and Walmart, are using embedded financial services to offer customers more convenient ways to pay for their purchases. For example, Walmart allows for payment through its mobile app, Walmart Pay, which allows customers to simply make purchases using their smartphone.
Technology companies: This one may not come as a surprise, but tech companies, such as Apple and Google, are using embedded financial services to offer financial products, such as credit cards and mobile banking apps. This allows them to expand their customer base and generate even more streams of revenue.
Telecommunications companies: Telecommunications companies, such as AT&T and Verizon, are using embedded financial services to provide direct access to financial services such as mobile banking and payment. This is great for increasing customer loyalty and retention.

The Takeaway

As we can see, it’s clear that embedded financial services will continue to play an important role in the economy during 2023 and in the years to come. As companies try to provide more value for their customers and streamline their operations, the integration of financial services into their products and services will continue to prevail.

The hardest thing to do as a company with long-standing operating methods is to implement change. However, in this day and age, change is inevitable for those who plan to capitalize on the modern market.

Written By Ben Brown

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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

ben brown

January 3, 2023

Business

When Machine Learning Meets Cyber Security

ben brown

January 3, 2023

Business

When it comes to the technology backing an enterprise, ensuring the security of networks and data has to be a high priority. This may sound glaringly obvious but oftentimes companies lack direction when it comes to building this kind of software. This is where cybersecurity comes in.

In today’s world, cyber security is non-negotiable for anyone with sensitive data stored online. Take for instance the fact that there are an estimated 2,200 cyber attacks per day which equates to over 800,000 per year and 1 every 39 seconds.

This raises a ton of red flags for many businesses that are now forced to evaluate their options for protection consistently. It's also important to remember that technology is built to adapt and needs to be in order to tend to the needs of its surroundings.

When it comes to the case of a company protecting its assets, there are many factors to consider both within and outside the organization. One way that companies are addressing the growing threat of cyber attacks is through the use of machine learning.

Machine learning is a technology that can perform without being directly ordered to carry out specific functions. As you may have guessed, this means its capabilities are incredibly versatile and you’d be right. When it comes to cybersecurity, here is how machine learning is used to enhance its performance:

3 Ways Machine Learning Is Used For Cybersecurity

Pattern Recognition: Machine learning uses algorithms that can realize patterns in data and identify any irregularities that might indicate a breach. Machine learning is especially effective at detecting malware, a type of hacker software designed to cause disruption. This is something that traditional methods may struggle to identify.
Attack Prediction & Prevention: Machine learning is used to predict and prevent cyber attacks before they ever happen. For example, an algorithm could be used to help a company that’s already experienced cyberattacks by analyzing the company's past attacks and identifying the common patterns or indicators that suggest a future attack. Companies can then take this information and develop contingency plans as well as take preventative measures.
Improved Security: Aside from simply detecting and preventing attacks, machine learning can improve the overall security of a company's networks and systems. For example, machine learning can be used to monitor network activity and identify potential vulnerabilities. This information can then be used to patch those vulnerabilities and strengthen the company's overall security posture. Features of this include:

Fraud detection: Machine learning can analyze data transactions and identify patterns that may suggest fraudulent activity.

Malware detection: As mentioned earlier, machine learning algorithms can also be trained to recognize patterns in code that might indicate the use of malware; this allows them to identify and block malicious software before it can cause any harm.

Phishing recognition: Machine learning can even be used to analyze the contents of an email and identify patterns of a phishing attack.

For Small Businesses:

Small businesses often have limited resources and may not have the same level of security as larger enterprises. However, this does not mean that they are immune to cyber-attacks. In fact, well over 40% of online attacks are targeted at small businesses due to their perceived lack of security measures. This makes it even more important for small businesses to have strong cybersecurity systems in place.

Machine learning can be a valuable tool for small businesses looking to improve their cybersecurity posture. By automating the detection and prevention of cyber attacks, small businesses can protect their sensitive data and ensure the long-term sustainability of their platforms.

The Takeaway

Machine learning can enhance cyber security by equipping companies with the most advanced tools and tactics to detect and prevent attacks, in addition to improving the overall security of the companies' networks and systems. As the threat of cyber attacks continues to grow, the use of machine learning in cyber security is inevitably going to continue expanding through innovation. If we were to get into specifics, experts are predicting a Compound Annual Growth Rate (CAGR) of nearly 9% over the next 5 years in the industry.

As we’ve seen here, machine learning is already a highly valued tool when it comes to security and preventative measures for cyber attacks or any other variant of digital fraud for that matter. Companies who are serious about the long-term sustainability of their platforms' secure information storage are making the necessary moves using the right software. But as always, it starts with reviewing the needs of your business to find what is right.

Written By Ben Brown

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ben brown

December 30, 2022

Business

Neural Networks: What is it? How is it useful?

ben brown

December 30, 2022

Business

Innovation is crucial for collaborative environments. Quick history lesson: It’s estimated that people have only been around for 0.007% of the time the world has existed. Considering this and looking around at the changes we’ve seen (most of which are thanks to technology) what resources do you expect to have at your disposal in the next decade?

In this world of technology, innovations that support data processing and analysis are highly sought after. Companies are finding ways to include these innovations in their software infrastructure to become more lucrative and scale for long-term success. To aid this demand for businesses, one of the hottest trends we’re seeing is that of neural networks.

What is a Neural Network?

This is a big term that many may be unfamiliar with, so let’s cover the basics of the concept. A neural network was inspired by the structure and function of an actual human brain and is a machine-learning model designed to perform like one. It’s made up of layers of interconnected "neurons," which are used to process and transmit information.

Every neuron in the system receives input from the other neurons, which will then process that input using an activation function, then the output will be sent to other neurons or an “output layer”. If I haven’t lost you yet, hang in there. The connection between these neurons is represented by “weights”, which are adjusted during the training process to optimize the performance of the model.

The kinds of tasks neural networks may be used for include:

Image classification: In this task, a neural network is used to recognize and classify objects in images. For example, different animals in a picture.
Language translation: A neural network can be trained to translate text from one language to another.
Forecasting: Neural networks can be used to make predictions about future events based on data from the past. For example, a neural network might be trained to predict patterns in the stock market or the weather.
Speech recognition
Natural language processing: Neural networks can be used to analyze and understand various languages.

These networks can learn and improve their performance over time by adjusting the weights of the connections between the neurons based on the inputted data. Are you seeing the connection to how a person's mind works?

How do Businesses Benefit From Them?

So far we’ve covered this technology using complicated broad terms and concepts which would make the average business owner wonder how this can be used to their benefit. The top 5 industries that benefit most from neural networks include:

Healthcare
eCommerce
Security
Finance
Logistics

Companies of all sizes and in various industries (not limited to those listed above) continuously find productive uses for neural networks. These uses will vary from industry to industry but here are 3 common thread benefits:

Decision-making: Neural networks can analyze a lot of data and identify patterns/trends that may not be immediately obvious to people. This helps businesses lean towards more informed and accurate decisions.
Efficiency: Neural networks can automate tasks and processes that would otherwise require the attention of experts. The system thus increases the efficiency and productivity of the business’ processes
Security: Neural networks can be used to identify and prevent fraud or other security threats by analyzing patterns and anomalies in data.

The main thing to glean from these points is that the way a neural network benefits a business is going to be subjective. Every company/industry has its unique approaches and processes that require various types of software to handle the job. What matters is that these aspects are identified and outlined before any implementation process begins.

The Takeaway

Neural networks can learn and improve their performance over time and offer many benefits to businesses that incorporate them. Software, as always, is going to involve analysis when developing a plan for how it can support a business. This is why consulting with software experts is highly recommended before starting your quest. Remember, experience either redirects or enhances instinct, never does it fail it.

Written By Ben Brown

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