How Quickly to Get the Product Conducive to Results?

These days, Fintech is becoming one of the world's fastest-growing industries. Every year, the number of new Fintech startups increases. In 2024, more than 13,000 such enterprises were recorded in the Americas.

Financial technologies include online banking applications, lending platforms, trading platforms, and robo-advisor apps for investing and budget planning. All these innovations demand top-notch Fintech solutions to serve customers, so aspiring Fintech startups need to find a talented team that can build an advanced product.

Key considerations when hiring

Before hiring a FinTech development company, it’s crucial to ensure the specialists have all the essential hard and soft skills. This way, the Fintech application project will have more chances to succeed. So, to hire reliable developers for your project, you need to understand the essential compliance requirements, which you will need to check. 

What are the must-have competencies of web development companies for Fintech?

• Technical knowledge. The company's developers must be well-versed in all the necessary coding languages, frameworks, technologies, and security norms. 
• Background in your niche. Check if a company has a portfolio of projects related to the financial field. 
• Familiarity with applicable finance legislation. The company must know industry norms such as PCI DSS and GDPR.
• Social skills. Seek a team that is able to ensure transparent communication, flexibility, well-organized teamwork, and attention to detail.

Experience in FinTech

Familiarity with the industry is obligatory for financial services development because the specialists must comprehend the main finance principles. A deep understanding of monetary standards and risk management allows developers to craft solutions that meet the niche requirements. Look for a company that has worked on apps or websites for mobile banking, budget management, cryptocurrency wallets, or other products related to Fintech app development. 

Technical Expertise

First of all, make sure the tech specialists know the necessary programming languages. For instance, frontend development for Fintech may require knowledge of JavaScript, libraries and frameworks such as React.js, adaptive and cross-browser layouts, as well as principles of responsive web design and great user experience. At the same time, Python paired with Node.js may be useful for backend development for Fintech.

Secondly, developers must ensure the product's security. To achieve this, they must understand finance APIs and data encryption protocols. Similarly, implementing secure coding methods and following best practices are critical to providing data security. Additionally, expertise in cloud and blockchain technologies can be vital for the safe and reliable launch and scaling of Fintech products.

In many cases, especially for lending platforms, a key functionality is digital credit scoring—the ability to assess a user’s creditworthiness based on real-time data automatically. Implementing such systems requires backend development expertise and experience working with financial scoring models, third-party data providers, and data compliance protocols. A development partner with proven experience building custom scoring logic can help you avoid regulatory risks and launch a more competitive product.

Security and Compliance Knowledge 

Fintech solutions must meet the latest regulations and standards for user data security. Since Fintech startups and online banks carry sensitive customer data, they are often victims of cyber attacks. That’s why there are legal regulations aimed at the safety of clients and ensuring secure accounting transactions. So, to get secure Fintech development services, check if a company has relevant experience and understanding of the best practices in data protection along with industry norms such as PCI DSS and GDPR.

Communication and Collaboration

Effective communication is crucial for successful Fintech website development. A simple and streamlined interaction provides quick response, the ability to adjust tasks quickly, and full transparency of their implementation. Also, the tech team members must evolve their problem-solving skills, be adaptable, and demonstrate excellent collaborative skills. The last thing can be reached only by an experienced provider, so you should assess the management style in a company.

Research Potential Partners 

Before hiring a Fintech development company, conduct research on your own. A company must have a website providing all the information about its expertise, portfolio, and contact details. Besides, don’t forget to check customers’ testimonials to determine the quality of the services provided. Likewise, case studies of Fintech projects will best demonstrate a company’s expertise and outcomes achieved. 

Develop a Request for Proposal

When you already have a candidate for possible cooperation, the next step is to write a project proposal. Initially, you need to define the project's goals, target audience, desired functionality, and budget. This process will help your possible contractor fully understand your business demands and provide you with a personalized offer.

Conduct Interviews

While conducting a technical interview, ask detailed questions about the development process to check your possible contractor’s skills. Secondly, the organization's leadership style and structure should be analyzed. Inquire about any cross-functional initiatives the company has worked on.

It’s also important to find out who is on the team. In addition to the usual members of a tech team, such as a technical lead, developers, and QA testers, there should also be an expert who works on UI/UX design for Fintech. And, of course, don’t forget to ask about their previous experience in the niche and evaluate their grasp of your business needs and vision of the product.

Portfolio Review

When selecting a software development company, experience is a key consideration. And the easiest way to assess it is by checking the portfolio. Companies that have been on the market for a while usually have numerous projects. However, you need to pay the strongest attention to those connected to the Fintech industry. Check for mobile banking apps, home loan calculators, or trading tools technologies to make sure their design, usability, and the achieved results are close to your goals and needs. 

Conclusion

Developing a Fintech product is not an easy task. Besides the fact you need to retain customers by providing an outstanding service, you also have to worry about things like regulations and data security. That’s why it’s essential to learn how to recruit decent Fintech development specialists. By following the clues given above, you will be able to find suitable candidates.

The implementation of artificial intelligence (AI) and machine learning (ML) has the potential to turn the healthcare industry upside down. And this global transformation is already beginning. Innovative technologies are being used in diagnostics and treatment. They are influencing pharmaceuticals, research work, and other aspects of medical practice.

Analysts at Global Market Insights have calculated that the AI market in healthcare is valued at $18.7 billion. However, by 2032, the situation will change. According to forecasts, its size will exceed $317 billion.

Considering this trend, we find it appropriate to explore the prospects that AI and the technologies based on it offer to medical professionals worldwide. We will also focus on the near future of AI-based medical solutions and discuss examples of digital products that are already available to specialists.

What Are Machine Learning Healthcare Projects?

Machine learning projects in the medical field involve the use of ML algorithms and models to perform various tasks for healthcare workers. These include:

• data collection and analysis;
• predicting the condition of patients and treatment outcomes;
• making decisions about the appropriateness of certain prescriptions, and more.

The effectiveness of using AI and ML in healthcare is confirmed by numerous specialized studies. Below are some examples:

• The Houston Method Research Institute used an AI-based tool for diagnostic purposes. Specifically, it was used to detect malignant breast tumors. The results shocked scientists. The system demonstrated a disease detection accuracy approaching 99%. Moreover, AI was able to analyze mammograms 30 times faster than humans.

• The Nationwide Korean Cohort Study focused on researching the effectiveness of predicting infectious disease outcomes using machine learning. The results showed that the accuracy of the applied ML model was over 90%.

• Scientists from Indiana University and Purdue University in Indianapolis developed an ML-powered system to predict the outcome of acute myeloid leukemia. The results of the model's tests demonstrated that it could predict remission with 100% accuracy. The likelihood of recurrence was correctly identified in 90% of cases.

• Researchers at BMJ Health & Care Informatics developed and tested a machine learning model for detecting NASH (non-alcoholic steatohepatitis). This disease is difficult to diagnose. However, the use of ML contributes to more accurate screening of NASH and allows for the timely application of effective treatments.

All of these statistics confirm the vast potential of machine learning in healthcare projects. So, what is this technology most often used for?

Popular Use Cases of AI Projects in Healthcare

According to Morgan Stanley Research, 94% of respondents (medical companies) reported using AI or ML in their practice.

Let’s take a look at which aspects of healthcare these technologies can optimize.

1. Pathology Detection

Machine learning models can be used to analyze various types of images. The goal is to recognize pathological conditions.

For example, they can be used to diagnose cancer, tuberculosis, and other infections.

How does this work in practice? Here are several examples of using ML in the field of pathology detection:

• A machine learning-based program analyzes tissue samples and helps identify malignant growths.
• ML algorithms recognize tuberculosis by examining sputum samples from an infected person.
• ML-based systems help differentiate between benign and malignant tumors.

An example of such software is AIM-PD-L1: an AI and ML-based tool that detects PD-L1. This protein is formed as a result of cancer cell development. Research on this solution showed that its results closely align with human diagnostics. Thus, the tool can be useful in identifying pathology.

Another solution from the same developer, PathAI, is the PathExplore panel for histopathological features. It can analyze tissue images at the pixel level. This allows it to examine millions of cells and accurately identify malignant ones.

2. Medical Imaging and Diagnostics

Healthcare machine-learning projects often use innovative technologies to analyze various images. These include CT scans, MRIs, mammograms, X-rays, and more.

Here’s how machine learning models can be used in diagnostics based on visual data:

• An ML-based system can analyze CT scan results and, with a high degree of accuracy, identify malignant tumors.
• Similarly, such platforms can diagnose ischemic strokes, such as hemorrhage, based on brain MRIs.
• Specialized ML-based tools allow for retina scans. This helps in the early detection of blindness due to diabetes, specifically diabetic retinopathy.

There are many similar solutions available on the market. Among them are Viz.ai and InnerEye.

The first, Viz.ai, is a care coordination system based on AI. It allows for automate detection of many diseases, including vascular pathologies.

The second, InnerEye,  is a solution from Microsoft that significantly speeds up work with patient images. For example, with this platform, doctors can accurately differentiate between healthy and cancerous cells. It also accelerates the start of treatment by 13 times.

3. Personalized Medicine

Each patient is unique due to the following characteristics:

• genetic predisposition;
• medical history;
• lifestyle;
• comorbid health issues, and more.

Machine learning-based platforms are able to take all these factors into account when selecting treatment methods.

Here’s how this can work in practice:

• ML models analyze genomic data and recommend individualized treatment methods for oncological diseases.
• Thanks to predictive analytics, AI-based tools can adjust insulin dosage. This is possible through constant monitoring of its level in the patient.
• Machine learning algorithms help choose the optimal antidepressant based on the patient's psychological state and genetic predispositions.

As an example, let's look at IBM Watson for Oncology. This is a machine learning-based system that considers many factors when recommending cancer treatments. These include the compatibility of prescribed medications, possible side effects, and the effectiveness of treatment in each specific case. As a result, the system provides healthcare professionals with several therapy options from which they can choose the most optimal.

4. Drug Discovery Process

The use of ML can accelerate the invention of new pharmaceuticals. Specialized systems show excellent results in discovering the medicinal properties of various substances. They can also analyze the interactions of drug components and optimize clinical trials.

AI in healthcare projects can be used for this purpose as follows:

• ML models accelerate the search for antibiotics and antiviral drugs.
• AI-based modeling helps reduce the number of failed clinical trials, thus saving time and resources.
• Machine learning algorithms can analyze the structure of substances, assisting in the development of new drugs.

A notable example of using AI and ML in creating new drugs is AlphaFold by DeepMind. This tool is designed to determine the structure of proteins, which are the foundation of biological processes in every living organism. Currently, the platform is successfully used for:

• fighting malaria;
• searching for a treatment for Parkinson's disease;
• counteracting bacteria resistant to existing drugs.

5. Epidemic Prediction

One of the capabilities of ML models is analyzing patterns and making predictions about outbreaks of various diseases.

Here are some examples of using ML-powered solutions:

• Such systems can predict global pandemics. To do this, they analyze data on population movement and disease statistics.
• ML algorithms track seasonal disease spikes, such as the flu.
• “Smart” platforms can predict outbreaks of specific diseases in high-risk areas. For example, they can forecast cases of malaria or dengue fever.

EPIWATCH is one of the leading examples of such platforms. This tool uses artificial intelligence to predict epidemics that could reach a global scale. The system collects and processes extensive infectious disease data in real time. It has proven effective in providing early warnings about potential pandemics.

6. Remote Patient Monitoring

In recent years, IoT (Internet of Things) devices have become widely used in medicine. These are objects equipped with sensors that can collect and transmit data wirelessly without human intervention.

According to Yahoo!Finance, the IoT market in healthcare, was valued at $58.8 billion in 2024, and by 2032, it is expected to exceed $305.5 billion.

This situation is explained by the fact that AI and ML enhance the efficiency of wearable devices and health monitoring systems in general.

The use of ML in remote monitoring includes the following cases:

• Smartwatches can track irregular heart rhythms and detect instances of sleep apnea.
• ML models can analyze ECG data in real time. Based on the obtained data, they can predict a heart attack.
• AI-based inhalers can track the patient’s readings and warn about an asthma attack.

There are many such solutions on the market. These include popular devices like the Apple Watch and Fitbit. These smart bracelets can monitor heart rhythms and detect uncontrolled breathing stops.

There are also more complex systems available to healthcare professionals. For example, SafeBeing from Somatix. Thanks to wearable devices, this platform responds to patient gestures and draws conclusions about their health condition.

7. Virtual Assistants

Not everyone can visit a medical facility in a timely manner. Moreover, many people need assistance and support after being discharged from the hospital. AI handles these challenges effectively. Developers are actively creating digital assistants with integrated ML algorithms. These assistants are capable of providing first-aid and consulting patients after treatment.

What can such tools do?

• These platforms help assess health status independently and suggest possible diseases.
• Smart chatbots help schedule doctor visits, remind patients to take medications, and more.
• Virtual assistants help monitor the progress of chronic diseases.

There are many such solutions available to patients. Let’s look at the features of Ada Health as an example. This platform helps assess symptoms and provide preliminary diagnoses using machine learning algorithms. It can:

• track changes in the body over time;
• provide recommendations regarding treatment and a healthy lifestyle.

8. Psychiatric Assistance

ML algorithms help diagnose and treat various psychiatric disorders, including those used in the fight against depression and anxiety.

Practical examples of ML usage in psychiatry:

• Intelligent chatbots can mimic real-world conversations. They can answer patients' questions and provide recommendations.
• ML models can analyze user behavior on social media and detect suicidal tendencies.
• These digital solutions can analyze human speech and identify early signs of Alzheimer’s disease.

Woebot is a smart chatbot that helps people with mental health disorders. It can assess a user’s mood and emotions. Among its functions are conducting conversations with patients and providing personalized strategies for coping with difficulties.

9. Robotic Surgery

Just imagine. Surgeons no longer need to be in the operating room. All procedures are performed by a robot. Sounds like a scene from a sci-fi movie? Nevertheless, this is our reality. Developers have already created robotic systems based on AI that enhance the accuracy of surgical interventions and reduce rehabilitation time.

What can such software do?

• Smart surgical systems can perform minimally invasive surgeries.
• AI-based software shows excellent results in neurosurgery and orthopedics.
• ML models predict potential surgical risks and possible complications.

Here, we cannot fail to mention the famous Da Vinci surgical robot created by Intuitive Surgical. This robotic system offers medical professionals incredible possibilities in the field of minimally invasive surgery. Its features include fluorescence visualization, integrated table movement, a vision cart, and more. This allows for precise and effective surgical interventions.

10. Automation of Administrative Tasks

Routine tasks such as organizing document flow or billing require a lot of time, diverting medical staff from their primary duties — treating patients. The implementation of machine learning-based software allows automation of these processes, reducing the time spent by medical personnel.

AI-powered systems are handling numerous administrative tasks:

• Platforms based on NLP can extract data from medical records and update patient electronic charts.
• ML algorithms analyze insurance cases, minimizing fraudulent claims.
• Smart scheduling systems efficiently manage hospital resources.

An example of such software is the Optum system. It offers numerous solutions for the healthcare business, including a platform for Revenue Cycle Management. Its capabilities include billing, increasing revenue efficiency, medical coding, and documentation.

Benefits of Machine Learning for Healthcare Analytics Projects

The use of ML models in healthcare allows for the analysis of large amounts of medical data. Furthermore, such models are continuously trained and improved. The implementation of tools based on these models brings a number of benefits to both medical professionals and patients:

Early Disease Diagnosis

Identifying serious diseases at early stages reduces mortality, improves the quality of life for patients, and increases the likelihood of full recovery or sustained remission. For medical institutions, this helps reduce their expenses and saves time.

ML models excel at early diagnosis. They analyze medical images, test results, and patient medical histories. They then track patterns and detect even the slightest deviations from the norm.

Improvement in Patient Care Quality

This is facilitated by AI-based digital solutions for personalized care, virtual assistants, and chatbots. They help maintain a person's health outside of the hospital, for instance, after discharge or before visiting a specialist.

At the same time, such platforms help reduce the workload on medical institutions. They can provide the patient with an initial consultation and allow medical professionals to remotely monitor the patient's condition.

Development of the Pharmaceutical Industry

Innovative technologies allow for faster development of new medications. This is a step toward effectively combating various diseases. This is particularly relevant during global epidemics. 

Cost Reduction

ML automates many processes in healthcare, from documentation management in clinics to diagnostics and treatment prescriptions. Additionally, the use of smart technologies helps reduce human errors and allows for some patients to switch to home care. As a result, medical institutions can increase the number of patients served and their profits.

Improvement in Treatment Effectiveness

In medicine, predictive models are used, which are trained on large sets of historical patient data. Such models can predict:

• the likelihood of recurrence;
• the need for hospitalization;
• the tendency for the disease to progress.

Medical professionals can use all this data to adjust the prescribed treatment to improve its effectiveness.

Key Challenges in Healthcare Machine Learning Projects

The implementation of machine learning in healthcare projects opens up many opportunities for the industry. We have already seen this today. However, alongside numerous advantages, some challenges may arise for innovators.

Security of Confidential Data

The digitalization of healthcare has led to a particularly sharp issue. We mean the preservation of medical confidentiality and the privacy of patient data. The use of virtual tools implies entering personal information into the system, which increases the risk of data leakage.

To mitigate these risks, maximum anonymity should be maintained. For example, financial and insurance data, as well as the patient's address, should not be included. It is also important to implement reliable data encryption methods and threat notification mechanisms on the platforms.

Quality of Data for Model Training

The effectiveness of ML models depends on the data input into the system. Inaccurate input data can lead to incorrect diagnoses, wrong treatments, and even death.

To prevent this problem, machine learning software should be used under the supervision of highly qualified medical staff.

Skepticism of Healthcare Professionals and Patients

Since machine learning projects in the medical field are still in the early stages of gaining ground, many doctors and patients are not fully confident in its capabilities. The main concerns include inaccurate diagnostics and the complete replacement of human involvement in medical practice.

To build trust, continuous training for healthcare professionals and patients is necessary. It is also important to notify people about the chosen treatment methods.

As for the complete replacement of human labor, this is highly questionable. Scientists believe that innovative technology will, in fact, expand the role of healthcare professionals. Their duties will include overseeing the operation of ML models and interpreting their results.

Difficulty of Integration with Existing Software

To ensure the smooth operation of medical institutions, new software products need to be seamlessly integrated into the existing infrastructure. The task becomes more complex because clinics may be using outdated systems that are not compatible with new technologies.

To solve this problem, investment in qualified IT personnel and gradual replacement of old software with new ones is necessary.

Technologies Used in Machine Learning Projects in Healthcare

Such high efficiency in AI projects in healthcare is achieved thanks to the wide range of technologies used in specialized software. Let's look at the main ones.

Computer Vision

This is the ability of a machine to understand and interpret visual information from the external world. In healthcare, it is used for analyzing medical images for subsequent diagnosis and treatment.

Examples of use:

• detection of tumors in X-rays, CT scans, or MRIs;
• analysis of biopsy results to identify malignant formations.

Robotic Process Automation (RPA)

This involves using bots (software robots) to perform repetitive tasks. In medicine, this technology is applied to automate administrative processes.

Examples of use:

• processing insurance claims;
• billing;
• inputting data into electronic patient records.

Logistic Regression

This is a statistical machine learning algorithm that is effective for predicting the probability of disease and classifying ailments.

Examples of use:

• assessing the risk of developing diabetes;
• determining the likelihood of cardiovascular diseases.

Support Vector Machines (SVM)

This is a supervised machine learning algorithm that is best suited for classification, i.e., separating data into different categories. It is used in medicine for diagnostic purposes.

Examples of use:

• differentiating between benign and malignant tumor cells based on biopsy data;
• classifying genes to identify genetic disorders.

Natural Language Processing (NLP)

This is a field of artificial intelligence that enables computers to understand, interpret, and generate human language. It is used to process medical text, such as doctor’s notes or specialized literature.

Examples of use:

• analyzing electronic medical records to extract patient data;
• virtual assistants and chatbots to assist patients outside medical facilities.

Large Language Models (LLM)

These are a specific type of machine learning models trained on large datasets. Their capabilities include understanding and generating human-like language.

Examples of use:

• answering complex medical questions;
• automating clinical documentation.

Deep Learning (DL)

A subset of machine learning that uses neural networks with multiple layers to perform tasks. It is applicable for image recognition and speech processing. It is widely used in medical imaging, as well as in fields such as genomics and pharmaceuticals.

Examples of use:

• developing new medications;
• diagnosing diseases based on visual data.

Reinforcement Learning (RL)

A type of machine learning where an agent learns by interacting with its environment, in other words, through trial and error. This technology helps optimize the effectiveness of treatment strategies.

Examples of use:

• robotic surgical systems;
• personalized treatment for various diseases.

The powerful potential of AI-based technologies suggests that the future of healthcare lies in them.

Future Trends in AI and Machine Learning in Healthcare Projects

It is predicted that in the coming years, AI and machine learning will continue to strengthen their position in healthcare. This is facilitated by:

• better awareness of these technologies among doctors and patients;
• an increase in the number of developers capable of creating and integrating innovative systems;
• a growing demand for skilled healthcare services and medications.

What can we expect in the near future?

Transformation of Primary Healthcare

AI and machine learning systems will take on more responsibility during a patient's initial visit to the doctor. They will classify patients based on their risk level, using examination results. Patients with a low probability of illness will be notified immediately. Those with a high risk will be referred to specialists.

This will reduce the waiting time for referrals and ease the burden on specialists.

Improvement of Healthcare Accessibility

According to the International Labour Organization, 56% of people living in rural areas have no access to healthcare. In cities, the situation is better but still concerning, with 22% of the population unable to access medical services.

AI and machine learning have the potential to fundamentally change this situation. This will be possible through telemedicine and mobile health services.

Focus on Data Security

AI system developers understand the importance of ensuring data security in healthcare. The HIPAA Journal states that in 2023, over 133 million medical records were exposed due to data breaches. This means the problem is becoming global in scale.

As a result, IT specialists will work on enhancing the reliability of the security measures being implemented.

Progress in Early Disease Detection

Modern intelligent software is capable of recognizing symptoms at the very early stages of disease development. However, in the near future, AI capabilities will go even further. The first steps in this direction are already being taken. An example is the Sybil tool developed by scientists from the Massachusetts Institute of Technology. It can predict the development of lung cancer within a year with 94% accuracy.

This is a huge breakthrough in healthcare. It has the potential to save millions of lives.

Emphasis on Personalized Treatment Plans

Doctors will increasingly move away from general prescriptions that do not account for the specific characteristics of individual patients. AI systems will be used to analyze medical histories, genetics, and other factors. Based on this analysis, personalized treatment methods will be selected.

This approach is particularly relevant for treating complex diseases such as cancer.

Darly Solutions Case Studies

Finally, we would like to share the experience of the Darly Solutions team. Our custom healthcare software development company has implemented many successful AI-powered projects for healthcare.

A company approached us with concerns about patient safety during the diagnosis of infectious diseases. They decided to rely on AI and machine learning for the accuracy and safety of their research.

The Darly Solutions development team created a platform for the client that can achieve 99.9% accuracy in clinical diagnostics. To achieve such impressive results, we used the following technology stack:

• Python for machine learning and data analytics;
• JavaScript and React for creating an intuitive user interface;
• WordPress for backend development.

We successfully implemented predictive tools, high-quality data visualization, and a user-centered UI/UX design in the diagnostic solution. As a result of our collaboration, the client’s goals were met. The company received a flexible, scalable, and highly efficient system.

Conclusion

It can be confidently stated that machine learning projects in the medical field are the future of global healthcare. Intelligent solutions have the potential to revolutionize disease diagnosis and treatment, patient care, and drug development.

Do you want to follow the latest trends and provide the highest quality services? Now is the perfect time to find an expert team to develop an AI and machine learning-based medical application.

The Darly Solutions team is ready to assist you with this! Contact our manager to discuss the project details and receive a digital solution that will exceed all your expectations.

What is a SaaS product?

SaaS (Software as a service) is a software distribution model in which a cloud provider hosts applications and makes them available to end users over the Internet. Therefore, SaaS applications are not set up and maintained by companies. A monthly subscription fee gives users access to the ready-to-use software. To make an analogy, the SaaS using process is like renting a vehicle, for which the lessor pays the price and uses it on demand. Nowadays, SaaS presents not as a service but as a necessary element at the center of the functioning of every second business.

Here are some statistics referring to SaaS software:

• The global SaaS market was valued at $237.48 billion in 2022, with projections to reach $908 billion by 2030, growing at a CAGR of 18.7%.
• Over the last seven years, the SaaS industry's size has increased by approximately 500%.
• The U.S. SaaS market is expected to grow at a CAGR of 5.64% from 2023 to 2028, reaching over $186 billion by 2028.
• By 2026, it's anticipated that public cloud spending will constitute over 45% of total enterprise IT expenditure.
• Large companies (with over 1000 employees) use about 150 different SaaS products, comprising 70% of their total software usage.

Top Industries

Leading industries in SaaS adoption include healthcare, financial services, manufacturing, and retail.  This trend highlights the importance of cloud-based solutions in driving business efficiency and innovation.

• Manufacturing: Projected to surpass $19 billion in SaaS market value by 2026, with increased adoption of cloud solutions for supply chain, inventory, and production planning driving growth.
• Web and Video Conferencing: Expected to reach $7 billion by 2026, driven by remote work practices and advancements in IoT, AI, and video compression.
• The healthcare SaaS market: Is projected to grow at a CAGR of 19.5% by 2028, driven by ongoing digitalization efforts, including the adoption of electronic health records (EHRs) and telehealth solutions.
• CRM (Customer Relationship Management): Expected to grow at a CAGR of 13.43%, reaching $59.4 billion by 2027, driven by higher adoption among SMEs, cloud-based solutions, and a focus on customer engagement.
• EdTech (Educational Technology): Projected to grow at a CAGR of 19.1% through 2028, spurred by increased demand for virtual learning platforms and educational content.
• HR (Human Resources): Anticipated to expand at a CAGR of 15.8% up to 2028, with growth fueled by the need for efficient management systems, talent acquisition tools, and advanced analytics.
• Property Management: Expected to see a CAGR of 15.5% until 2028, driven by demands for automated rental management and enhanced tenant experiences.

Top Players

To get a clearer idea of what a SaaS product is, here is a list of the biggest SaaS companies known around the world:

• Microsoft Corporation;
• Adobe Inc.;
• Figma;
• HubSpot;
• Trello;
• Zoom;
• Slack.

Suppose you have experience using one of the above products. In that case, you can imagine how it works, why it is convenient, and how important it is to implement such SaaS platforms in your business.

Our company also deals with similar SaaS development services. We implemented the latest technology to build an innovative financial CRM system and booking beauty appointment platform, according to SaaS approach.

Benefits of using SaaS products

So, what are the benefits of this system for you? If you clearly understand what function SaaS products perform and your business purpose, you can formulate precise requirements for the end result of using this service. This section will list the main things that SaaS providers can offer for B2B and B2C businesses.

• You pay only for the resources and functionality that the company actually uses. There is no need to pay for the full range of services that a SaaS provider offers you. You're free to choose the necessary and critical features for your business and pay only for them.

• Economic benefit. This point can be viewed from two angles. First, you know exactly how much you'll have to pay for the subscription period, and you won't have surprises with the increased cost of the resources you've already paid. Second, you won't risk your investment in such a subscription. If your business needs a more powerful or extensive platform to deploy your capacity. You'll end your subscription to one product and switch to another without losing money.

• Accessibility to your team from anywhere. Any device your employees use - PC, laptop, mobile phone, tablet - can use the SaaS product.

• Shared use of the software by the entire team. There is no need to buy a subscription for each user within the same company; the only exception may be the limit of connected users.

• Centralized software updates. There is also no need to worry about updating the paid platform manually; the providers will do everything for the customer.

Business prospects with SaaS products

Enterprises incorporating SaaS products into their business concept are uniquely positioned to benefit from this growing trend. Here are some key business prospects with SaaS products, supported by statistics and numbers:

• Cost savings: SaaS service reduces businesses' need to invest in expensive hardware and software, which leads to cost savings. As a result, companies can save up to 20-30% of their IT costs.

• Scalability: According to 68% of companies, scalability is one of the main benefits of using SaaS. Therefore, a company can quickly increase or decrease its use of software as needed. 

• Security: SaaS products offer enterprises advanced security features to reduce the risk of data breaches, including encryption and multifactor authentication. 94% of enterprises report a noticeable increase in security after implementing SaaS.

• Competitive Advantage: 74% of companies claim SaaS gives them an advantage through the quick and cost-effective deployment of innovative software.

Summary

As you have already understood, SaaS products are not only an opportunity to effectively manage business processes and business as a whole but also a practical necessity, without which it will be easy to optimize work processes and implement large-scale projects without a platform for necessary control. 

By understanding what working principles SaaS is based on, you can maximize the potential of SaaS in your business and achieve greater success by 2030 and beyond.