We’ve reached a point where you cannot imagine a financial institution without its mobile app version. The difference between the mobile banking app development of today and before is immense.
Yet, technologies like Artificial Intelligence, Machine Learning, and Natural Language Processing take the functionality and user experience to a whole new level.
Why the implementation of them is so complex and costly? What benefits do these technologies bring to both financial institutions and their customers? And how does the maintenance of apps that have these technologies integrated look?
Let’s answer all these questions!
A lot of benefits, a lot of challenges, and a lot of money to implement
Implementing AI, ML, and NLP technologies into your banking app is a complex and expensive project.
To function properly, technologies like these require:
- Significant computational power
- Sophisticated algorithms
- Large datasets to work with
Mobile banking app development process cannot be completed without extensive research and experimentation together with hours and hours of finetuning. None of these stages can be overlooked because they guarantee that the algorithms will perform accurately and without errors.
Then goes the integration of these technologies with existing systems and again, more tests to make sure they’re compatible with various platforms you operate on.
In short, here’s where you’ll pay the most:
- Hiring data scientists, ML and NLP specialists
- Buying high-performance hardware and software
Getting cloud computing services and data storage are on the list of ongoing expenses, without them none of those technologies will function. Regular updates are another recurring expense on the list. Regular updates are important to ensure stable performance and solid security.
More about maintaining AI, ML, and NLP
Maintaining a banking app that incorporates AI, ML, and NLP technologies requires ongoing effort and resources because these technologies need to be continuously monitored, updated, and optimized.
AI and ML models will be neither accurate nor relevant if not regularly updated. For them to be as such, you need to:
- Retrain these models
- Fine-tine their parameters
- Deploy updates
Apart from that, you’ve got to have continuous monitoring of them so that any issues that arise are detected and addressed promptly. Some common problems of AI and ML models are so-called model drift and performance degradation.
As for NLP systems. Updating them is even more important because it’s the only way they’ll be able to understand new terms, languages, and dialects. Without an update, an NLP system won’t process natural language inputs effectively.
Hiring a fintech app development services provider lowers the cost and complexity of implementing AI, ML, and NLP technologies in a banking app significantly. That is so because they’ve got three key elements that make the deployment of these technologies efficient:
- Expertise
- Tools
- Resources
Ethical considerations on the usage
Regulatory standards are what business owners think about in the first place when it comes to personalization and the general utilization of such technologies. However, the ethical implications of using AI, ML, and NLP are as important as legal ones.
These technologies benefit you and your customers only when used responsibly. This includes avoiding biases in AI models and maintaining transparency with customers about how their data is used.
Ethical considerations are the foundation that maintains customer trust and prevents reputational damage.
The advanced tech of today gives your business benefits of the future
Administrative and operational efficiency is what you get with these technologies. AI, ML, and NLP technologies contribute to enhancing administrative and operational efficiency, each in its own way.
An example of Artificial Intelligence that’s out in the clear sight is an AI-powered chatbot. It uses natural language understanding to manage vast volumes of customer inquiries and learns from each interaction to improve responses.
AI’s algorithms also analyze workload patterns and predict optimal task assignments. Its scheduling and resource allocation optimization suggest ways for your team to be as productive as possible.
Machine Learning algorithms are often used in banking to automate data entry by recognizing patterns in unstructured data. Machine learning algorithms reduce errors and enhance speed drastically. They classify and extract information from invoices or forms and that way automate tedious back-office tasks.
Implementation of ML models enhances fraud detection processes too. ML analyzes transaction patterns and identifies anomalies in real time, reducing the risk of financial loss.
NLP’s role often lies in processing and analyzing customer feedback. It extracts sentiment and key topics that you can later visualize to get a quick insight into a particular aspect.
Another common use of NLP is the automation of document management with the goal of improving accuracy and retrieval times. NLP extracts and classifies information from extensive text data, such as contracts and reports, contributing to that.
These examples are only a small part of what these technologies do:
- Save time
- Cut operational costs
- Enhance accuracy
Advanced technologies work in pairs with your staff, freeing up its resources to focus on tasks that require human attention.
How will these technologies change the user experience of my clients?
The first thing is that your customers get an app that gives them a more personalized banking experience.
AI will be analyzing their behavior to offer financial advice and product recommendations that resonate with their preferences. AI also gives your clients excellent, 24\7 customer support.
AI-powered chatbots and virtual assistants handle routine inquiries and troubleshoot issues without the need for human intervention. A timely yet quality assistance improves overall satisfaction and enhances trust and loyalty to your company.
Machine Learning also personalized user experience, but in terms of things like user interface, for example. Its precise analysis based on individual usage patterns makes the app more intuitive and user-friendly.
These algorithms can also analyze customer data to identify various issues, financial or organizational, to offer solutions before they escalate.
NLP often works in pairs with AI-powered chatbots and virtual assistants to make sure that language interactions are natural and smooth. Thanks to NLP, interactions are more convenient and accessible too! That is because your customers can interact with the app through voice or text commands.
Conclusion
AI, ML, and NLP are complex and expensive, but if you approach their implementation with specialized expertise and sufficient resources, you’ll end up with a high-quality app that both your customers and your team appreciate.