Financial apps are everywhere now. They have become so popular that people often have more than one installed on their smartphones. These apps allow users to manage their finances and keep spending in check.
But users want to get even more out of financial software, which means one thing for financial companies: they must go out of their way to offer a unique user experience.
Let’s see how financial software vendors can apply Artificial Intelligence to their apps to offer better insights to users and gain a competitive edge.
Artificial Intelligence and Machine Learning are two of the most influential trends in financial software today. The ability of AI to analyze trends, make predictions, gather insights, and offer solutions to problems has proved beneficial for many fields of business.
Artificial Intelligence makes analyzing big volumes of data and pulling relevant insights easy, which puts it in the spotlight with the financial industry.
AI and ML have proved useful to a wide variety of financial operations, such as banking and credit offerings, asset management, personal financing, and so on.
Let’s take a look at some of the most helpful applications of AI/ML in financial planning.
Data-driven decision making
One of the most important factors in financial planning is making timely decisions.
Obviously, it’s hard for humans to accurately analyze the vast amount of information available to predict trends.
This is where software powered by cutting-edge technology comes in handy.
AI-powered software is great at identifying patterns from sifting the data it gathers. Its inherent efficiency at handling large volumes of information helps it make the most accurate predictions on, for example, market behavior.
Artificial Intelligence can be used in many ways to help users get the most out of a financial app. Among those are:
Automated loan approvals;
Investment management assistance;
Automated operations based on pre-determined rules.
In addition to improving workflow, it’s essential for financial software to guarantee the security of all transactions. One field Artificial Intelligence and Machine Learning can help with is detecting fraud.
How does AI do it?
AI can analyze transaction information, find inconsistencies in different payment documents, and identify fraudulent behavior. Using all of the data available, the software is able to detect any kind of unusual activity.
What’s even more effective is that it can apply predictive analytics to alert users about the potential risk of fraud.
The financial sector, just like any other field of business, aims to improve the experience of customers. The secret lies in understanding the problems customers face and coming up with effective solutions.
With the help of Artificial Intelligence, the software can more effectively analyze users’ information and their behavior, such as comments, clicks, and transactions.
All of that data can be used to improve interaction between the user and the software, as well as the product offerings.
And there’s one more benefit: As customers continue to interact with the software, AI will get better at recognizing their behavioral patterns and offering the right services. Regular customers can expect better advice on asset management, more accurate risk calculation, flexible rates, and more.
It’s obvious that traditional customer support is not perfect. And here’s why:
Queues are long;
It’s hard to explain certain problems over the phone;
Chatbots are clueless for the most part; all many of them do is redirect the customer to endless FAQs.
But Artificial Intelligence can improve the situation, making solving customers’ issues quick and efficient without the need for a customer service agent to engage.
Powered by AI, advanced chatbots are able to apply natural language processing to understand the customer’s problem, whereas regular chatbots can only understand a certain number of phrases and struggle to grasp complex sentences.
Automation can happen through the chatbot. For instance, chatbots can manage claims from users, conduct transactions, and review information that the customer provides.
All of this can be done without human intervention, which removes the need for a customer to wait their turn on the phone. This allows customer support agents more time to address more complex issues.
So, what about those more complex issues?
Even though AI-powered chatbots are more powerful than ever before, they can’t do everything. In some cases, customers might still need a real human’s assistance.
But here’s the next best thing: AI chatbots can analyze the issue and assess its complexity on their own. If they decide they can’t help with the problem, they will send the customer to a support agent.
Financial operations are conducted on the basis of huge volumes of data from a wide variety of sources. Managing it could be a nightmare.
Sure, paperless management makes this more convenient, but it’s not enough. There is simply too much information to deal with, and too much possibility of wasted time and human error when analyzing it.
Artificial Intelligence makes a huge difference.
Natural language processing, text analysis, data analytics, and predictive analytics allow for extracting deep insights beneficial to the decision-making process.
Artificial Intelligence and Machine Learning have many applications in financial software, but some might still view applying it as a risky investment.
Let’s take a look at the real benefits of using AI in financial apps:
With advanced chatbots, financial software vendors are able to offer 24/7 customer support that is quick and effective at solving customers’ issues.
Reducing repetitive work
Powerful algorithms allow for automating simple tasks, saving time, and sparing staff a lot of repetitive work.
Eliminating human error
Software always makes decisions based on data and never on emotion. It helps to keep users from acting irrationally.
Automation and fast but accurate decision-making helps even a small team of specialists be more effective. On top of that, advanced risk management and fraud detection protect against financial losses.
The flagging of fraudulent activities and timely alerts help customers keep their finances protected.
Examples of Financial Planning Apps That Use AI
There are plenty of financial software developers that have already recognized the power of Artificial Intelligence and Machine Learning in finance management.
Here are just some of them:
Talking finance management app
Cleo is a personal finance management app. Available for iOS and Android, it provides personalized spending breakdowns and budgeting tips.
It can be connected directly to the customer’s bank account and supports sending money through Facebook Messenger.
In addition to all those smart things Cleo can do, it allows customers to sort of talk to the app about their budgeting.
Cleo uses an AI-powered chatbot that can review the customer’s budget. The customer then can ask whether they can afford a certain purchase and receive an answer.
Gamified personal finance assistant
MintZip is also a financial app that has gone far beyond just showing transaction information. The app uses conversational AI as a companion that can help users better manage their finances and have a good idea of how much money they have.
In addition, the app provides budgeting tips and even sets challenges for its users: for instance, it can challenge them to save 10% of their monthly income for half a year.
Fintech solution with auto-saving
Wizely is another financial assistant. Like other apps, Wizely monitors the customer’s spending and provides information in the form of dashboards.
It also offers an auto-saving feature that allows the user to set a goal and then helps with daily, weekly, or monthly savings.
Based on the information it gathers, the app provides personalized finance management tips.
How Much Does It Cost to Create an AI-Driven Personal Finance Assistant?
Now that we’ve covered some of the benefits and applications of Artificial Intelligence and Machine Learning, it’s time to talk about the costs of actually creating finance assistance applications.
The first thing you should know is that there is no universal pricing for finance software development.
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To know how much exactly you will spend on developing it, you need to keep in mind major variables that influence the cost:
Features desired in the personal finance assistant;
Development team composition and rates.
Let’s review each of these separately to see how they impact the overall cost of the software project.
Main features for a personal finance assistant
The main difference between a regular personal finance app and an assistant is the ability of the latter to learn using information it pulls from the user’s bank account.
Not only does it display transactions and let the user track savings, it also gives tips on how to effectively manage the user’s budget.
So, what are the features that let a finance app act as an assistant?
Financial data management. An AI-powered finance app is able to chew through large volumes of data quickly and deliver important insights to the user.
Conversation engine. Another common feature is Natural Language Processing, which lets the app understand human language. This makes interaction between the user and the software easier by shortening the learning curve.
Financial coaching. Using predictive analytics and big data, the app is able to analyze transaction info and offer financial management tips. This is helpful for users struggling to save or just stay within their budget.
The logic is simple: the more of those advanced features you build, the higher the cost of development.
Now, let’s talk about the development team.
Development team composition
Having found a software development company that will build the project for you, you may be presented with a couple of different cooperation models.
The way you cooperate can also influence the cost of the software.
A dedicated development team is the most effective cooperation model, so let’s take a look at how such a team is composed.
Keep in mind that when working with a dedicated team, you can alter the cost of the project by scaling the size of your team.
Who’s on your team depends entirely on what you need for a project. Roles can include:
Business analyst: Checks the financial feasibility of the project and helps to create the project specification.
Project manager: Estimates the scope of work, breaks the project into tasks, assigns those to the developers, and keeps the client updated.
Developers: Build the software solution.
QA testers: Check whether the app runs well.
Depending on your project needs, you can hire a full-fledged team or just select a few specialists.
Below is a rough estimate of costs for a personal finance assistant. The actual cost will vary depending on your project needs, so be sure to contact us for a definitive cost breakdown.
As you can see, applying AI in financial applications is very beneficial for companies looking to take their financial app to the next level: you can improve security, save operational costs, and boost customer satisfaction.
If you want to implement Artificial Intelligence in your finance planning app or build a software project from scratch — talk to us.
We have extensive expertise in building AI- and ML-powered financial software solutions and have successfully carried out numerous projects for the financial sector.
Head of Production
Oversees all production processes and manages the Quality Assurance department. Develops the company’s IoT, AR, VR, AI, and ML expertise.
AI-powered means that the software solution uses Artificial Intelligence algorithms to perform some of its functions. Those might be:
Predicting changes or trends;
Offering solutions to problems.
How is AI used in finance?
Some of the most popular applications of AI in finance are
Data-driven decision making;
Customer experience improvement;
What is an AI assistant?
An AI assistant is a piece of software that not only presents users with information but also actively offers advice. In the case of financial apps, the assistant provides budgeting tips, saving plans, and so on.
What technology is used in AI?
AI heavily relies on the following tech:
Big Data for gathering and analyzing information;
Predictive analytics for making predictions and offering corresponding actions.
How is Machine Learning used in finance?
Machine Learning is often applied to personal finance software to learn the patterns of users’ behavior. This information can be used by the software providers to improve their products, offer better deals, and personalize the customer experience.
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