4 ways AI affects the financial industry

by James · December 15, 2022

Cutting-edge technologies have become an important part of the financial sector. Given their enormous reach and the huge number of benefits, more and more market players are embracing fintech, so that the industry is growing and developing impressively day after day.

A notable milestone in fintech has been the deployment of artificial intelligence algorithms, a potential game changer that has revolutionized the financial industry in many ways.

The benefits of AI are enormous: this smart technology reduces unnecessary expenses, streamlines financial management and increases revenue for businesses and people, to name a few.

Moreover, the world market for artificial intelligence is huge: in 2020, it has already exceeded 62 billion dollars and is expected to increase by more than 40% annually. As for AI in the fintech market, the numbers are also proving very promising: with a total value of $8 billion in 2021, AI technology is expected to exceed $27 billion by 2026.

In this article, let’s look at how AI is impacting the financial sector, discover the use cases and see how they are disrupting the industry. Watch.

AI impact on the financial industry

Artificial intelligence represents a number of advantages that financial organizations are able to exploit. Let’s take a look at the most prominent ways AI is impacting finance:

Ensuring customer satisfaction is a task of utmost importance, yet quite challenging, given the rapidly growing demands and expectations of customers today.

With the financial market being one of the most competitive, those managers who implement AI can win over new customers and retain existing customers due to better service.

AI opens the door to convenience and a better financial management experience, making financial services customer-centric and personalized.

Cost reduction and optimization of workload

It is impossible to underestimate the contribution AI makes to the workflow in finance.

By using AI, companies can eliminate a huge amount of manual and repetitive work and streamline many business processes. Once done, they will be able to reduce many operating costs, increase accuracy and allow employees to redirect their efforts to strategic business tasks that require human effort.

As fraud grows like a weed and cybercriminals’ tactics constantly change, financial institutions are looking for ways to increase security and protection.

AI-based fraud detection takes security to a new level and solves problems effectively, keeping financial data and money under effective protection.

Data-driven decision-making

Whoever has the data has the power. Although it is a universally recognized truth, it is particularly applicable in finance, where the amount of information increases every hour.

With AI capabilities to accurately process vast amounts of data, financial organizations can have a helicopter view of many business aspects, allowing them to build strategies and make data-driven decisions.

With all this in mind, we will look at the impact of AI in fintech through the lens of prominent AI examples.

Top AI use cases in fintech

Chatbots as virtual financial advisors

These days, chatbots are miles ahead of what they have been in the past. They have become really smart and can act as digital assistants.

With just a few clicks or taps, users can instantly get support, guidance on financial operations, spending insights, personal advice on services, savings plans and deposit options – all without queuing or waiting on the phone for human assistance.

According to a recent study, chatbots are expected to save banks 862 million working hours, and that will be up to 7.3 billion dollars globally.

AI is also a powerful tool for credit scoring. Previously, the loan application was assessed for two or three weeks and took the time and effort of many different specialists.

Now AI solutions can assess potential borrowers in time, at lower costs and with better precision as it takes into account more data and therefore more factors. Therefore, companies can now better assess applicants’ creditworthiness and offer personal loan options.

In an industry that is heavily dependent on collecting and processing large amounts of data, the use of AI is a clear game changer. Machine learning, a subset of AI, painstakingly collects each data set without missing a single point, processes this vast amount of data and forms accurate reports and predictions.

They help companies see early signs of potential problems and enable them to always be one step ahead and take timely action to protect themselves from threats that could affect their overall functioning or revenue.

Service providers have made the security of their clients a top priority, and AI is helping them with that. Financial institutions are implementing AI-based systems in record numbers, with more than $217 billion spent on AI applications that help prevent fraud and assess risk.

The AI-based system quickly detects suspicious behavior, verifies user identity and responds immediately to cyber attacks that threaten customers’ accounts.

With ML power to quickly process and analyze massive amounts of data in real time and the ability to learn from experience, security models can be significantly improved.

Robotic process automation is a perfect tool to increase productivity, improve accuracy and speed, and reduce operating costs. Forward-thinking companies are realizing the value, and according to the Gartner report, nearly 80% of CFOs have already implemented or plan to implement RPA.

Artificial intelligence-enabled software can take over and handle repetitive, high-volume tasks that previously required humans to perform. Use cases of RPA include, but are not limited to, purchase orders, invoices, payroll processing, reconciliation of accounts and financial reporting.

Organizing a successful AI journey

As we can see from the above, there are several proven examples of effective applications of AI in fintech. Nevertheless, it is important to organize the implementation wisely in order to achieve successful business results. Deloitte suggests six steps for a successful AI journey. Let’s take a look at them.

Step 1: Develop an AI strategy

First, managers must define what needs to be done. Here, everything depends on the company’s specific goals and how they align with its mission and vision.

Second, define how it can be achieved. It is important to specify how:

• Correct data can be made available

• AI culture can be established across the company

• AI applications can be integrated into existing workflows

• AI processes and applications remain ethical and reduce risk

Step 2: Define a use-case-driven process

Deploying AI for AI’s sake can result in failure. Managers must define business value-driven use cases and prioritize them into the roadmap. Creating a diverse portfolio of relevant achievable AI projects and deploying “low-hanging fruit” first can help ensure a more robust deployment.

Step 3: Experiment with prototypes

Use cases for AI and ML require prototypes to be scaled up to the enterprise, and therefore thorough planning and strategy are essential. Teams need time to learn and explore which solutions work best and fit overall broader goals. It is also important to find out if a prototype can be integrated with the existing company’s ecosystem or create synergies with other prototypes, or if scaling up provides more value, etc.

Step 4: Build with confidence

It is advisable to address risks and deal with compliance regulations at early stages. It will help to keep implementation up, ensure trust for all stakeholders and get sufficient value from AI. To better compete, leaders should also seek to expand AI capabilities and explore new data sources.

Step 5: Scale for enterprise implementation

Organizations need to build the right talent mix to ensure successful and smooth AI adoption. Using AI models, integrating them with current processes and modifying them as business processes change can help move closer to enterprise-wide adoption.

Step 6: Create sustainable results

After AI adoption, organizations should maintain the ecosystem, monitor AI models and how they respond to various inputs to identify ways to improve results and refine the models. Organizations can also open up new business opportunities by simply reusing existing AI models to improve a wider range of processes and enhance existing AI applications.

Final thoughts

Artificial intelligence is rightly considered a game-changer for the financial services industry – it not only improves a business from both internal and customer sides, but also saves costs, providing valuable benefits that keep you always one step ahead.

With AI driving the future, more and more companies are resorting to implementing this cutting-edge technology to be competitive in the market and win over customers.

Since the financial market is highly competitive, implementing AI is important, as staying away from technological advancements can cost more in the long run.