Is AI Replacing Developers?

Is AI Replacing Developers?

Is AI replacing developers?

Artificial Intelligence (AI) is transforming industries across the board, and the tech industry is no exception. The question on many minds is: will AI replace software developers? While the answer is complex, it’s important to understand the potential impact AI can have on productivity and the future of software development. 

The Impact of AI on Productivity in Software Development

AI has the potential to significantly enhance productivity in software development. By automating repetitive tasks, AI allows developers to focus on more complex and creative aspects of their work. Tasks such as code generation, bug fixing, and even some aspects of software design can be streamlined with AI, leading to faster development cycles and higher-quality outputs.

Is AI replacing developers?

How AI Will Transform Software Development

AI will not replace software developers; instead, it will transform their roles. Here are some key areas where AI is making an impact: 

  1. Automated Code Generation: AI tools can generate boilerplate code, reducing the time developers spend on routine coding tasks.


  2. Intelligent Debugging: AI can identify and fix bugs more efficiently, improving code quality and reducing the time spent on manual debugging.


  3. Enhanced Testing: AI-driven testing tools can run extensive test cases, identify edge cases, and ensure robust software performance.


  4. Project Management: AI can assist in project management by predicting timelines, identifying potential bottlenecks, and optimizing resource allocation.


  5. Learning and Adaptation: AI systems can learn from past projects, continuously improving their performance and providing developers with valuable insights.

The Evolution of Developer Roles

The automation of manufacturing processes in the past provides a valuable reflection on what we might expect in software development. Just as automation in manufacturing led to the evolution of certain roles, AI in software development will likely evolve the roles of developers. 

Senior developers and specialists are more likely to be affected by AI advancements than junior developers. While junior developers will continue to perform essential tasks, senior developers will need to adapt to new roles that leverage AI tools to enhance productivity and drive innovation.

Addressing Challenges and Ethical Considerations

At Scio, we are aware of the challenges and ethical considerations that come with integrating AI into software development. 


  • Data Privacy: Ensuring that AI systems handle data responsibly and comply with privacy regulations. 
  • Bias mitigation: in AI algorithms to ensure fair and equitable outcomes. 
  • Job Displacement: Addressing the potential displacement of jobs and providing opportunities for upskilling and reskilling. 

Ethical Considerations: 

  • Transparency: Maintaining transparency in how AI systems make decisions and handle data. 
  • Accountability: Ensuring that there is accountability for AI-driven decisions and their impacts. 
  • Bias and Fairness: Actively working to reduce biases in AI systems and promoting fairness. 

Is AI replacing developers?

Embracing AI as a Tool

At Scio, we are starting to use like ChatGPT as part of our programming and languages toolkit. By leveraging AI for clear prompts, token management, and feedback loops, we enhance our development processes and ensure that our teams remain at the forefront of technological advancements. 

AI is not here to replace software developers but to empower them. By embracing AI, developers can enhance their productivity, focus on more meaningful tasks, and drive the future of software development. At Scio, we are committed to navigating the challenges and ethical considerations of AI integration while leveraging its potential to benefit our clients and teams. 

Robotic Process Automation and the future of intelligent machine work

Robotic Process Automation and the future of intelligent machine work

Curated by: Sergio A. Martínez

The reason why humans build machines is that they want to make work easier and faster. That always has been true; machines help us accomplish tasks that would otherwise take a long time with just human labor alone, or even be impossible for a human to do in the first place. They also help us save space, energy, and time — after all, resources are precious commodities, so if we can utilize them more efficiently through machines, why wouldn’t we? And more importantly, machines also increase our industrial production rate, more so than what could be achieved without the use of machines. Humans often look to make activities effortless, and advances in technology give us the capability to automate tasks.


And of course, this process of automating tasks and processes is pretty important in every industry imaginable. Let’s look, for example, at software development: A solution already in popular use is Robotic Process Automation (RPA), a way to automate specific tasks within a process, so people don’t have to do them manually. The main advantage of RPA is that it can save time and be more accurate than humans because it’s not necessary to have someone actively monitoring how the task is performed, and ultimately means that businesses can get more done faster and with fewer resources. This allows developers to focus on more complex projects while reducing the time spent performing mundane tasks.

By its very nature, RPA works well with larger applications due to its ability to organize data into streamlined processes, reducing the overall development time and cost, reducing development hours, and making sure everything runs smoothly. Robots make this easy as they don’t need the same amount of troubleshooting, testing, and debugging time as we humans do. In other words, the reason why RPA has become an increasingly popular tool in the software industry is because of its ability to speed up development for faster technology deployment. As stated by IBM:

“[RPA] combines APIs and user interface (UI) interactions to integrate and perform repetitive tasks between enterprise and productivity applications. By deploying scripts which emulate human processes, RPA tools complete autonomous execution of various activities and transactions across unrelated software systems.

However, with more and more businesses migrating to digital tools and platforms, and software development continues rapidly expanding with no signs of slowing down, the demand for innovative technology solutions also grows. It’s no wonder the development of automatic tools is booming to keep up, helping to optimize tasks during a software project in a way that was unthinkable barely a decade ago. There is no bigger leap forward in automation technology than Artificial Intelligence, which promises to change the field in ways that we maybe cannot grasp yet.

Automatic Intelligence

Empathy Design Disorder 1

The use of AI technology is certainly booming at the industrial scale and with good reason. By deploying these kinds of applications, businesses can automate many mundane, time-consuming tasks that would otherwise require a lot of manual labor, while reducing wasted resources and increasing efficiency in the production process. With AI driving efficiency gains, businesses benefit from reduced labor costs and improved production times, making it a no-brainer as far as implementation is concerned. 

It’s no surprise, then, that use of AI technology is booming. This capability has generated enthusiasm from those who understand its vast capabilities, leading to an explosion of use at an industrial scale. And as AI continues to expand, it may become a fundamental component of modern business operations around the world. However, is the implementation of AI tools and an automation process the same thing? Or do these ideas refer to fundamentally different concepts with distinct goals and desired outcomes?

AI is not the same as automation. Automation is a machine executing a series of instructions exclusively set by humans. If an action isn’t explicitly described in the instructions, the machine can’t do it. With AI, however, the machine can take broad rules outlined by humans, and determine its own pathways to success”, explains the Artificial Intelligence Institute. “Automation can be used in tandem with AI such as machine learning and deep learning to produce even better results in a process we might call AI automation [which] allows us to reap both the business process benefits of automation — increased speed, efficiency, time-savings, and ability to scale — with the insights, flexibility, and processing power of AI technology.

That way, AI is revolutionizing the robotic automation process and has opened up virtually infinite possibilities for all sorts of industries, enabling robots to react faster and make more accurately timed decisions without direct human input. AI can even give robots the ability to learn from their mistakes, so they don’t repeat them and cause unnecessary delays in production or other processes. All of these advantages offered by AI give RPA tools a new lease of life, making them even better players in today’s automated world. And this can only get better, right?

The “artificial” in Artificial Intelligence

Robotic Process Automation 4

It might seem cut-and-dry to think that AI is an overall net positive on automation processes, but companies should approach AI with caution instead of putting too much trust in it, outright replacing manual decision-making processes without due consideration, because there are often large discrepancies between initial expectations and actual outcomes when working with AI. In other words, while these new tools may promise optimal performance, they don’t always live up to expectations, so any organization interested in these kinds of automation tools needs to bear the limitations of AI in mind at all times.

When companies place too much confidence in AI, they may miss key opportunities to inject creativity or human judgment into decision-making processes which can lead to misguided actions with unintended consequences”, says Adolfo Cruz, PMO Director, and Partner at SCIO. “For example, AI tools are limited when it comes to making decisions; they can only provide insights based on data and algorithms, and do not possess the same level of judgment as a human. Additionally, these tools lack intuition and creativity and may not be able to think outside the box or come up with creative solutions to unique problems”. 

That is to say, AI has come a long way in developing industrial advancements, yet there are still certain tasks that should be left off limits. AI should not be involved in any decision-making processes due to their lack of understanding of the potential implications of their actions. Allowing the robots to take over tasks such as operating complicated machinery and making decisions over them could do more harm than good when it comes to safety measures for both the workers and the products being created. Even with the best technology and programming, mistakes can still be made due to inevitable flaws in their programming. These risks outweigh any saving benefits that AI machines may provide, therefore we must prevent them from causing any further damage by restricting them in what they can do within an industrial context.

In short, automation and AI represent a powerful combination of resources with exciting potential. With no tedious tasks to weigh them down, people can focus their full power on the challenge or problem at hand and work in tandem with AI automation to create dynamic systems that save time, energy, and money. This combination is already being used across industries to great effect — streamlining production processes that were once complex and solving problems more quickly than was ever thought possible. All of this leads us toward an exciting future where these amazing technologies will continue to do even more positive things for both businesses and consumers. All in all, it’s truly amazing how much these two forces are capable of when we use them together.

The Key Takeaways

Robotic Process Automation
  • The point of building machines is to reduce the amount of work a person needs to do to produce something, and in software development, this is no different.
  • It should be clear that AI and Automation tools do not refer to the same concept, exactly, but should be combined to get the most out of them.
  • The main advantage of AI is that it can make its own decision and correct courses, which can be powerful when used with RPA.
  • However, this AI application should be careful and considerate, or any organization runs the risk of over-rely on this technology, which can have unintended consequences.

Scio is an established Nearshore software development company based in Mexico that specializes in providing high-quality, cost-effective technologies for pioneering tech companies. We have been building and mentoring teams of engineers since 2003 and our experience gives us access not only to the knowledge but also the expertise needed when tackling any project. Get started today by contacting us about your project needs – We have teams available to help you achieve your business goals. Get in contact today!

Tech and AI: Trends to watch out for in the coming year of 2023

Tech and AI: Trends to watch out for in the coming year of 2023

Curated by: Sergio A. Martínez

Without question, the wide strides that AI technology has made in the last few years have brought it from a niche novelty to a serious force in the tech sector, enabling the development of tools that will change the way we use and create software, art, and writing, as well as bringing a level of efficiency that could change the industry as we know it. Truly, 2023 seems to be the year where AI will leave a profound mark, and being aware of what to expect will be important for an industry where change can happen overnight.


We’ll see a lot more AI-enabled devices in our homes and workplaces, as well as a continued increase in the use of AI for things like facial recognition and target marketing. We may also see some interesting new applications of AI, such as using it to create more realistic virtual worlds or to help automate complex financial transactions. Of course, there will also be plenty of challenges to overcome, such as ensuring that AI systems can operate safely and ethically. But I remain optimistic about the future of AI, and I think it has the potential to truly transform our world for the better.

The AI of today and tomorrow

AI in 2023 3

AI is still in its early developmental stages, which means that a limited number of tools are available for implementing AI. However, some of the most important tools currently available include natural language processing, which is used to analyze and interpret human language, which is essential for developing intelligent assistants and chatbots, predictive analytics to make predictions about future events, trends, and behaviors, and machine learning, used to create algorithms that can learn and improve from experience. These three tools are essential for developing AI applications and will become even more important as AI technology advances. With these rapid developments, AI will become one of the most important tools in various fields, and the trends for 2023 will include…

1) A wider democratization of this tech

AI is no longer the exclusive domain of scientists and engineers. Today, anyone with an internet connection can access powerful AI tools and resources. This has leveled the playing field, allowing people from all walks of life to create and experiment with AI. This increased accessibility has already had a transformative effect on the world, and it is only going to become more pronounced in 2023 and forward. As AI continues to evolve, it will become an increasingly important part of our lives, changing the way we live, work, and interact with the world around us, with a level of quality expectation that will certainly affect the products and services offered by almost every type of business.

2) The rise of prompt engineering

The topic of prompt engineering is one we have touched on before, doing an overview of how this field will become a career in demand in coming years thanks to the increasing popularity of AI tools for general consumption. For those not in the know, prompt engineering is a “Natural Language Processing” area, where you design the final output of an AI system by carefully constructing the instructions for its generative engine. With an application in everything, from art to coding, prompt engineers will become a very in-demand position for organizations heavily investing in AI toolsets, so 2023 will bring a very interesting change in the job landscape.

3) No-code platforms and other generative tools

Speaking of which, generative tools are here to stay. With applications like Dall-E opening to the general public, generative tools are becoming a unique way of approaching problem-solving, allowing their users to explore and experiment with different possible solutions. This approach is well-suited to the rapidly changing field of artificial intelligence, where new challenges and opportunities are constantly emerging. Additionally, generative tools can help to automate the process of creating training data sets, which is essential for machine learning. As AI generative tools become more sophisticated, they are likely to play an increasingly important role in the advancement of artificial intelligence in 2023.

4) Ethical AI

Artificial intelligence is often lauded as a transformative technology that has the potential to revolutionize industries and change the way we live. However, AI also raises significant ethical concerns, which need to be addressed to ensure this technology is as useful and safe as possible. One of the most pressing issues is the lack of diversity in the field of AI, which creates a risk of bias being built into algorithms, as we have seen before, for example, with Application Tracking Systems within the HR field, which tend to be easily gamed, proving inaccurate (or worse) when selecting the appropriate candidate for an open position.

Another major concern is data privacy. After all, AI systems are becoming increasingly adept at gathering and analyzing data. In some cases, they may even be able to eavesdrop on our conversations or track our movements. As a result, there is a real risk that our personal information could be leaked or mishandled.

Finally, there is the issue of transparency. Due to the complex nature of AI algorithms, many experts fear that the opaque nature of AI could be exploited for nefarious purposes, such as mass surveillance or even control of public opinion. Furthermore, the rapid pace of development in AI is outstripping our ability to understand and regulate it, and as a result, there is a real risk that AI could be abused in ways that we cannot even imagine. For these reasons, 2023 will be an essential year to demand greater transparency from those who are developing and deploying AI technology. Only then can we hope to prevent its misuse.

5) Leaps in autonomous systems

In a world that is becoming increasingly reliant on technology, it is no surprise that autonomous AI systems are gaining in popularity. These systems can perform tasks that would normally require human involvement, such as monitoring inventory levels or providing customer service, and even historically riskier fields, like transport and industrial applications are becoming more reliable and cheap enough that 2023 will likely see mass adoption of these tools in everyday tasks. The clear advantage they have by operating around the clock without needing breaks, and their lack of susceptibility to emotions or fatigue is a huge advantage over traditional systems, helping to improve efficiency and productivity in a variety of industries. As more businesses begin to recognize the benefits of these autonomous systems, their popularity will likely continue to grow in the coming year.

Final notes

AI in 2023

With all this said, 2023 is shaping up to be the “Year of AI”. The sheer amount of data that will be generated by businesses and individuals will continue to grow exponentially, allowing for better training of AI algorithms, and making them more accurate and efficient. And the cost of computing power and storage will continue to decline, making it more accessible to businesses and organizations of all sizes, with breakthroughs in AI technology enabling humans and machines to work together more seamlessly than ever before. And as long as AI’s ethical concerns will be addressed more seriously, this technology will be increasingly ubiquitous in daily life, both inside and outside of work. All of these factors together make 2023 the year that AI will come into its own and begin to transform the world as we know it. Are you ready to embrace it?

The Key Takeaways

  • The rapid evolution of AI technologies has made them a viable tool in plenty of industries, and 2023 can be a landmark year in its adoption.
  • Although still in the early stages of development, disciplines like natural language processing, machine learning, and predictive analytics have pushed AI into mainstream usage.
  • Specifically, AI will increase the popularity of prompt engineering, generative tools, and autonomous systems.
  • This democratization of this tech, while bringing innovation at an unprecedented pace, also has ethical concerns that the industry needs to solve to guarantee a safe implementation of AI in daily life.

Scio is an established Nearshore software development company based in Mexico that specializes in providing high-quality, cost-effective technologies for pioneering tech companies. We have been building and mentoring teams of engineers since 2003 and our experience gives us access not only to the knowledge but also the expertise needed when tackling any project. Get started today by contacting us about your project needs – We have teams available to help you achieve your business goals. Get in contact today!

Customer support in FinTech: Is AI the best answer for it?

Customer support in FinTech: Is AI the best answer for it?

Curated by: Sergio A. Martínez

The impact FinTech is having on the way we live and manage our finances cannot be overstated: from mobile apps helping us with our budget to platforms that revolutionized how we make and receive payments; the way we interact with our money has changed, to the point that is pretty much unthinkable to not have access to these financial services anytime we need them, with customer support that matches it.


The critical role of Customer Support

Now we can bank, invest, and make payments 24/7 from the comfort of our own homes, and with a few clicks of a button, transfer funds overseas without having to pay high fees or wait for days for the transaction to clear. Moreover, many banks have started offering free or low-cost mobile banking services to compete, giving us more choice than ever when it comes to finding the best financial services.

However, this rapid change in how our personal finances work has brought a challenge; the customer support in these FinTech applications needs to be top-notch, making the user feel secure and welcome, ensuring they have everything they need, and enabling them to take full advantage of what these platforms have to offer.

After all, good customer support is important in any industry, but it’s especially critical in the world of FinTech, a relatively new field still growing and evolving, which means that there’s still some uncertainty around it, and customers rely on companies to provide a clear and helpful service. In addition, FinTech products are often seen as complex and confusing, so a good customer support flow can help to build trust and confidence and can help to differentiate one company from another. In a crowded and competitive market, good customer support can be a key advantage. But what is the best approach to it?

A task made for AI?


As businesses increasingly adopt AI technology, the question of whether or not to use it for customer support is becoming more relevant. There are some clear advantages to using AI for customer support, such as around-the-clock availability and the ability to handle large volumes of inquiries, which are uniquely important when it comes to FinTech applications. With AI-powered chatbots and virtual assistants, they can provide 24/7 support without the need for human employees, and these automated systems can often handle simple inquiries more quickly and efficiently than a human agent. In addition, AI can be used to analyze customer data and identify patterns that may help these platforms to improve their services.

“Customer support is one area where AI can play a significant role, helping automate support tasks like answering FAQs or processing customer requests”, says Rod Aburto. “In the future, AI may even be used to proactively detect and resolve potential customer issues before they cause problems and analyze customer behavior and preferences to provide personalized recommendations or advice. So, where we stand now, the possibilities of AI are promising, likely making customer support more efficient and effective.

However, AI also has important drawbacks. Chatbots and virtual assistants are often unable to handle more complex questions or requests, which can frustrate customers and lead them to seek out human assistance anyway, defeating the purpose of these solutions, and AI systems may not be able to replicate the empathy and personal touch that humans can provide, increasing the potential for miscommunication, running the risk of alienating users.  

Consequently, as businesses weigh the pros and cons of using AI in customer support, they will need to decide whether the benefits outweigh the risks, and ultimately, the decision of whether or not to use these technologies for customer support comes down to what is most important for the business. If speed and efficiency are the primary priorities, then AI may be the best option, but if the human connection is key, then a traditional customer support approach may be preferable. With that in mind, what are the expectations of the user base about customer support that you should integrate into the overall service? What is the image of “good customer support” people have in their minds when they think of FinTech?

  • A sense of control:

    According to Zendesk, “People want to feel a sense of control about their money and financial transactions. The same could be said about their customer support experience. Data shows that 69 percent of people prefer to resolve as many issues as possible on their own before contacting support”, and the proper help and support, having all the information they will need in a single place, is how you empower your users and make them feel in control of their money.

  • Consistency of the service.

    This encompasses everything from a consistent message in every channel (avoiding conflicting information that might frustrate a user), fast and agile response times with little variation, safeguards in case of server problems, and clear communication and transparency with every issue that might become present. What you want here is a specific experience that the user can expect when having any questions or issues.

  • Clear navigation paths.

    Be it automated chatbots, FAQs, hotlines, tutorials, or even a simple account activation, the customer journey should be planned upfront, and every platform should offer clear labeling with as few steps as possible to ask or troubleshoot something, open to user feedback, that has available all the information expected from them. “If your user has to go to outside sources to solve an issue, your customer support has already lost”, explains Rod Aburto about the critical importance of this point.

  • The option of human interaction.

    Although most of these points can be supported by good design and virtual assistants, having the option to talk directly to a person is something still valued by most users, especially if they have ongoing questions and concerns about the service. Having someone on the other end capable of answering and explaining the finer points of an inquiry is still unmatched in customer support.

Keeping the best of both worlds

Customer support in FinTech Is AI the best answer for it 2

There’s no question that AI is reshaping the customer support landscape; by automating simple tasks and providing access to vast amounts of data, AI can help businesses deliver faster, more efficient customer support, but that still leaves some things that only humans can do, as our last point shows. Traditional customer support teams bring a deep understanding of the customer experience, alongside the ability to build personal relationships with customers, which are invaluable in the delicate work FinTech applications often do. So a mix of both approaches, as the Helpware blog notes, might be the best course: 

For AI in clients’ support, you will not substitute people but leverage AI to expand the services. The sporting chance for customer support companies is to combine AI and the workforce. Merging autonomous programs, speaker recognition, and online with people-based client support leads to customer retention. Therefore, AI in clients’ support needs to work together with rather conventional domains.

As we have discussed elsewhere in our blog, AI is a tool that, while capable of automating many daily tasks, shines when paired with an expert that can utilize its benefits to their maximum advantage. And when these two approaches are combined, businesses can create a truly world-class customer support operation, where AI can handle simple tasks quickly and efficiently, freeing up human agents to focus on more complex issues, and also providing the personal touch that automated systems can’t match.

It’s not uncommon to receive automated customer support when calling a company these days, but it can be frustrating when you need to talk to a real person, which is why this provides the best of both worlds: the speed and efficiency of automation, with the human touch of a real person, allowing companies to offer a more personalized service, with AI gathering data about customers that can then be used by support representatives, so they can offer unique insights into the needs of customers. Overall, this is a win-win situation for both businesses and customers.

After all, what good customer support should offer, in both FinTech and elsewhere, is the ability for the users to feel a certain degree of protection, with the tools and processes necessary to make the whole experience as smooth as possible. And with the rapid growth of FinTech platforms and the increased accessibility that comes with it, these kinds of services are more critical than ever; a lot of the users will be accessing financial services for the first time, so questions, issues, and challenges are to be expected. Because FinTech is doing more than revolutionizing how we think about our money; it’s safeguarding our finances, and the responsibility that comes with it cannot be understated. And sometimes, all that is needed is a friendly voice willing to help on the other side of an app.

The Key Takeaways

  • The revolution of FinTech is changing how we think about finances, making services more affordable and accessible than ever before.
  • However, this popularity of FinTech solutions also has challenges, especially for the users accessing these kinds of products for the first time, with customer support being one of the most critical.
  • AI can be a great tool to deal with customer support, offering availability and quick responses in almost any area, but it probably be not enough at this point; human-based customer support will still be important.
  • That’s why a mixed approach might be the best choice, empowering human agents to help customers better and faster, with all the details handled by an AI assistant.

Scio is an established Nearshore software development company based in Mexico that specializes in providing high-quality, cost-effective technologies for pioneering tech companies. We have been building and mentoring teams of engineers since 2003 and our experience gives us access not only to the knowledge but also the expertise needed when tackling any project. Get started today by contacting us about your project needs – We have teams available to help you achieve your business goals. Get in contact today!

Is the future of FinTech in the hands of Artificial Intelligence?

Is the future of FinTech in the hands of Artificial Intelligence?

Curated by: Sergio A. Martínez

When most people think of Artificial Intelligence, they probably conjure the Hollywood depictions of evil robots that become sentient or self-aware, and then go out of control. However, even if this is the general pop culture stereotype of AI, the real-life technology is very different, already getting implemented in several key industries to streamline processes and improve efficiency. The Financial Technology sector is no different; AI is starting to have a major impact, from chatbots and digital assistants to complex algorithmic trading, reshaping the way we conceive and manage our finances.


And it’s not just big banks and financial institutions that are benefiting from the power of AI — FinTech startups use it to create innovative products and services that change the way we bank, invest and save, bringing more personalized experiences to customers by analyzing their spending patterns and providing tailored recommendations. 

So, while AI continues to evolve, its increasingly significant footprint on the FinTech industry is making financial services more efficient and accessible, playing a crucial role by helping to automate various tasks, from customer service to fraud detection. For example, AI-powered chatbots can provide personalized financial advice, and Machine Learning is being used to develop predictive analytics tools that can help identify investment opportunities, or red flags to watch out for.

As a result, AI will only become more embedded in FinTech, transforming the financial sector by automating processes and providing real-time insights, and helping cut costs. Perhaps most importantly, AI will give organizations the ability to make sense of the vast troves of data they collect daily, using Machine Learning algorithms to identify patterns and trends that would be impossible for humans to spot. As a result, this technology is poised to profoundly impact the financial sector in the years to come.

However, the popularity of AI solutions in FinTech has also raised questions within the industry; after all, with machines driving more financial systems, is their traditional risk management ready to keep pace with an increasingly automatized approach that brings innovations and disruption almost daily? For any business, it’s important to stay ahead of the curve and make sure you are using AI to benefit your customers and your bottom line, and risk assessment needs to be considered to succeed at it. But what will it look like?

Risk assessment in FinTech: A job for robots?

As FinTech companies become increasingly reliant on AI, the need for robust risk management processes is more important than ever. By its very nature, FinTech is a highly innovative sector, which means new risks are constantly emerging. And while AI and Machine Learning can help to identify these risks promptly, as well as monitor any exposure to them by automating data analysis and providing accurate results, gaps still exist.

One of the main concerns is that AI systems can be biased against certain groups of people, due to the data that they are trained on. If an AI system is only exposed to data skewed in favor of one group of people, it will produce biased results”, says Rod Aburto, Service Delivery Manager and Co-Founder of Scio. “And beyond that, AI systems can be hacked or tampered with, which could lead to disastrous consequences. These tools are still far from perfect, and there is always the possibility that they’ll make mistakes with serious implications, but despite these potential risks, AI remains promising.

Combining Machine Learning and AI can help financial institutions to make more informed decisions, like assessing the impact of new regulations on a business model or identifying risks associated with their products and services, but the availability of data is an important matter: the final assessment is only as good as the information entered into the system. This means FinTech companies need to be especially vigilant in their risk assessments due to the rapidly changing nature of the information. For example, if a training dataset is predominantly male, a Machine Learning algorithm may learn to associate certain traits with being male, leading to biased results when applied to a broader population. 

In the specific case of FinTech, a way that bias can be introduced is through algorithms that have been designed to achieve a particular outcome, rather than being impartial. An algorithm designed to identify creditworthy individuals is likely to be biased against low-income applicants, leaving people in need of these kinds of services out. 

Another challenge for FinTech companies reliant on AI to provide their services is that they are vulnerable to attacks by hackers who could exploit weaknesses in these systems. Hackers could gain access to sensitive customer data or even manipulate the algorithms used by FinTech companies, leading to disastrous outcomes. So when it comes to FinTech, security should be a top concern, and data breaches and security vulnerabilities can have a devastating impact on both consumers and businesses when machines cannot make critical decisions on their own. That’s why it’s so important for fintech firms to have robust security measures and know the risk involved in pure automatization.

A balanced future in FinTech


FinTech is an industry constantly evolving, and there are many good practices around risk assessment, but at its core, risk assessment in FinTech should focus on three key areas: regulatory compliance, customer impact, and business resilience”, continues Rod Aburto about the challenges in pure automatization. “And when it comes to implementing AI tech in this area, responsibility, due diligence, and human intervention are key to ensure they work as intended. These machines are more than capable of digesting data and coming up with insights than almost any person but are not infallible. Without human expertise guiding and implementing actual useful results, it’s easy to render any outcome from AI less than ideal.

AI is revolutionizing the fintech industry at every level, from software development to customer service. According to the International Data Corporation (IDC), “the worldwide market for AI software, hardware, and services is expected to surpass $500 billion by 2024”. As more FinTech companies are using AI to create new and innovative products and services that make financial services more efficient and accessible, it’s important to keep in mind that an ethical, responsible, and effective implementation of AI is one where humans are kept in the loop and can be made accountable for any mistake. AI will only become more embedded in FinTech as the industry evolves, and for businesses, it’s important to stay ahead of the curve and make sure you are using AI to benefit your customers and your bottom line.

So, by conducting responsible and human-curated risk assessments, FinTech companies can stay ahead of the curve and make sure that their products and services are as safe and secure as possible.

The Key Takeaways

  • The FinTech sector is one of the most innovative, and technologies like AI and Machine Learning are finding implementations in almost every area.
  • AI brings a speed of automation of processes, insights, and results without precedent, and while exciting, challenges arise alongside these tools.
  • Traditional risk assessment needs to keep pace with the technological revolution and has to keep a watch out for weaknesses and biases in these systems.
  • Human intervention and interaction will still be necessary for the foreseeable future, guiding these systems and achieving their best outcomes.

Scio is an established Nearshore software development company based in Mexico that specializes in providing high-quality, cost-effective technologies for pioneering tech companies. We have been building and mentoring teams of engineers since 2003 and our experience gives us access not only to the knowledge but also the expertise needed when tackling any project. Get started today by contacting us about your project needs – We have teams available to help you achieve your business goals. Get in contact today!

The future of Artificial Intelligence in software development: Bringing the best of both worlds, today.

The future of Artificial Intelligence in software development: Bringing the best of both worlds, today.

Curated by: Sergio A. Martínez

It’s no secret that software development is a rapidly evolving field. Every year, new tools and technologies are being introduced that promise to make developers more productive, and recently, AI has emerged as one of the most promising for software development. We have talked before about how unlikely (at least for now) that AI programs and tools could completely replace a software developer, but that doesn’t mean that these applications can’t have a profound impact on the way we develop software.


In fact, alongside no-code platforms, AI is starting to change the way we approach development from the ground up, requiring new skills to make the most of these technologies. AI-powered tools can help developers write code faster and more accurately, and AI-based software testing and debugging tools can help to find and fix software defects more quickly. So, as this technology continues to improve, more AI-based tools will likely be adopted by software developers. However, how will this profession need to evolve in order to adapt to the AI revolution? What skills will be needed, and what repercussions can AI have in the long term?

Prompt engineering: A new skill to master.

As AI continues to become more advanced and available for the average developer, ‘prompt engineering’ will also become a basic skill for the future programmer.

But what is prompt engineering? Simply put, it’s the process of designing and creating prompts that can elicit the desired response from an AI system. For example, if you are planning a chatbot intended to provide customer support, you would need to create prompts that would help the chatbot to understand the customer’s issue and provide an appropriate response. While AI systems are becoming increasingly capable of understanding natural language, they still rely on prompts to some extent. 

In a way, prompt design is like playing a game of charades”, explains the essay “Prompt Engineering: The career of the future?” by Shubham Saboo. “We give the person just enough information to figure out the word using his/her intelligence. Similarly, with [the language prediction model] GPT-3, we give it just enough context in the form of a training prompt to figure out the patterns and perform the given task.” 

As a result, prompt engineering is an essential part of using AI applications, and anybody looking to work with these tools must master them to use their full capabilities in the most efficient way possible. In fact, this skill is becoming more common in all kinds of creative areas, from software to art, where AI applications are getting popular., for example, can generate full written content with the correct prompt, and even tools like DALL-E 2 can generate just about any image using the right combination of words. And when it comes to software development, the tools necessary to make this process faster and more efficient are becoming reality.

The machine is my Copilot


Codex is a family of AI models from Open AI that translates between natural language and code in more than a dozen programming languages”, says the Microsoft GitHub page on prompt engineering. “The power of these AI models is that you can quickly develop and iterate on your ideas and build products that help people do more.

Ultimately, AI applications like Codex could drastically improve the efficiency of software development, making it possible to create better software in less time. If you’re a developer, chances are you already know about GitHub because it provides an easy way to share and collaborate on code. But what you may not know is that GitHub also offers a tool called Copilot, based on Codex, which can be a big help when it comes to writing better code. Copilot is a tool designed to create code suggestions from plain English prompts (for example: “Write a function that adds two numbers and returns the result”), giving you a hand in solving complex coding puzzles, showing you what the code could look like, or what you might need to make it work as intended. It also includes features like code snippets and templates, which can save you time when working on projects. Oege de Moor, Vice President of GitHub, explains Copilot the best:

Because it was trained both on source code and natural language, you can write a comment in English, and then Codex will suggest the code that follows. The technology works similarly to auto-complete in word processing but writing code instead of plain language and completing whole functions at a time. When you type in your editor, an excerpt of your code is sent to the AI, which response with ways to complete what you’re typing.

This could also work as a great teaching tool for people starting to learn to code, streamlining the process and letting its users’ experiment and prototype in a particular language, giving them the chance to test and start over in a very efficient way. Also, it works as a great alternative to no-code platforms where you can hit a ceiling in terms of what you can create, letting a user devise a specific solution by engineering the correct prompt to obtain it.

However, there’s still a long road ahead before Codex, Copilot, or other similar tools become a normal part of software development. For starters, there’s the issue of copyright, a problem shared with DALL-E and other art-generated tools, which have to feed from already created content to generate a result. In the case of Copilot, this means that the AI has been trained on GPL code stored in its servers, which is proven controversial, especially in Open-Source circles that point out that their code can easily be laundered with AI for commercial applications. 

With issues like this, it’s important also to start thinking about the discipline and good practices that these tools will require to actually add up to software development. From where we stand today, AI tools are here to stay, and learning to use them the correct way, or understanding the ramifications they will bring to our industry (reportedly, GitHub is working on an “origin tracker tool” to detect copyright violations, for example), there’s no doubt that the next few years of software development will change radically. 

With an AI companion at your side, whether you’re just getting started with coding or you’re an experienced developer, tools like GitHub Copilot can be a valuable resource for improving your productivity and writing better code. With that in mind, what will you be able to achieve tomorrow?

The Key Takeaways

  • AI tools are here to stay, and it’s important to be ahead of the curve and see the opportunity they bring in software development.
  • One of the most important skills to develop is ‘prompt engineering’: the ability to tell a machine exactly what you need to get the best result possible.
  • Software development is already changing thanks to AI-based coding tools like Codex and Copilot, which promise to make the process of development faster and more efficient.
  • Discipline and good practices are going to become a necessity to use these tools to their full potential while due diligence and copyright considerations will become a ‘must have’.

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