AI in fintech: 4 ways AI impacts on financial industry

Cutting-edge technologies have become an essential part of the financial sector. Given their vast outreach and the enormous number of benefits, more and more market players embrace fintech, so the industry grows and develops impressively day after day.

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

The benefits AI brings are huge: this smart tech reduces unnecessary expenses, streamlines financial management, and increases earnings for businesses and people, to name a few. 

What is more, the world market for AI is immense: in 2020, it has already surpassed $62 billion and is expected to increase by more than 40% annually. As for AI in the fintech market, the figures also prove to be very promising: with an overall value of $8 billion in 2021, AI technology is expected to surpass $27 billion by 2026.

In this article, let's look at how AI impacts the financial sector, discover its use cases and see how they disrupt the industry. Stay tuned.

AI impact on the financial industry

Artificial intelligence represents numerous advantages that financial organizations are capable of leveraging. Let's take a look at the most prominent ways AI impacts finance:

• Better customer service

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

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

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

• Cost reduction and workload optimization

It's impossible to underrate the contribution AI makes to the workflow in finance. 

Using AI, companies can eliminate a vast amount of manual and repetitive work and streamline many business processes. When done so, they will be able to reduce many operational costs, increase accuracy, and allow employees to redirect their efforts to strategic business tasks that require human input. 

• Enhanced security

As frauds grow like a weed and cyber criminals' tactics constantly change, financial institutions seek ways to amplify security and protection.

AI-based fraud detection takes security to a new level and addresses issues effectively, keeping financial data and money under efficient protection. 

• Data-driven decision making 

Who has the data has the power. Although it's a universally acknowledged truth, it is especially applicable in finance, where the amount of information increases every hour.

With AI capabilities to accurately process extensive 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 that in mind, we'll look at the impact of AI in fintech through the lens of prominent AI examples.

Top AI use cases in fintech

•  Chatbots as virtual assistants

These days chatbots are miles ahead of what they've been earlier. They have become really smart and can act as digital assistants. 

With just a few clicks or taps, users can instantly get support, guidance in financial operations, spending insights, personalized advice on services, saving plans, and deposit options – all of that without standing in lines or waiting on phone for human assistance .   

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

• Loan underwriting

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

Now, AI solutions can assess potential borrowers in a timely fashion, at lower costs, and with better precision as it takes into account more data and, thus, more factors. Therefore, companies can now better evaluate their applicants' creditworthiness and provide personalized loan options.

• Risk management

In an industry heavily reliant on gathering and processing large amounts of data, the application of AI is a definite game changer. Machine learning, a subset of AI, meticulously collects every single data set without missing a spot, processes this extensive amount of data, and forms accurate reports and predictions.

They help companies to see early signs of potential issues and enable them to always be one step ahead and undertake measures on time to keep safe from threats that might impact their overall functioning or revenue.

• Fraud Detection

Service providers have made the security of their clients a number one priority, and AI helps 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 risks.

The AI-based system quickly detects suspicious behavior, verifies user identity, and instantly responds to cyber attacks that threaten clients' accounts.

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

• Process Automation

Robotic process automation is a perfect tool for boosting productivity, improving accuracy and speed, and reducing operational costs. Forward-thinking companies realize the value, and according to the Gartner report, nearly 80% of financial leaders have already implemented or are planning to implement RPA. 

Artificial intelligence-enabled software can take over and handle high-volume repetitive 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-mentioned, there are several proven examples of effective applications of AI in fintech. Yet, it's essential to organize implementation wisely to arrive at successful business outcomes. Deloitte suggests six steps for a successful AI journey. Let's take a look at them.

• Step 1: Develop an AI strategy

Firstly, executives need to define what needs to be done. Here everything depends on the company's specific goals and how they align with its mission and vision.

Secondly, define how it can be achieved. It's essential to specify how:

 • The right 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 the sake of AI can result in failure. Executives need to define business-value-driven use cases and prioritize them into the roadmap. Creating a diverse portfolio of relevant AI projects that can be achieved and deploying 'low-hanging fruit' first can help ensure a more robust deployment.

• Step 3: Experiment with prototypes

AI and ML use cases require prototypes to be scaled up to the enterprise, thus thorough planning and strategy are crucial. Teams need time to learn and explore what solutions work best and fit overall broader objectives. Also, it's essential to figure out whether a prototype can integrate with the existing company's ecosystem or create synergies with other prototypes, or if scaling up brings more value, etc.

• Step 4: Build with confidence

It's advisable to address risks and deal with compliance regulations at early stages. It will help keep implementation up to speed, ensure trust for all stakeholders, and derive sufficient value from AI. Also, to better compete, executives should seek to expand AI capabilities and explore new data sources.

• Step 5: Scale for company deployment

Organizations need to build a correct talent mix to ensure successful and smooth AI adoption. Employing AI models, integrating them with current processes, and modifying them as business processes change can help approach company-wide adoption.

• Step 6: Drive sustainable outcomes

After AI adoption, organizations should maintain the ecosystem, monitor AI models and how they react to different inputs to identify ways to improve results and refine the models. Also, organizations can open new business opportunities by simply repurposing existing AI models to improve a broader range of processes and enhancing existing AI applications.

Final thoughts

Artificial intelligence is rightfully considered groundbreaking for the financial industry – not only does it improve a business from both internal and customer sides but also saves cost, and provides valuable advantages that make you always one step ahead.

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

As the financial market is highly competitive, implementing AI is essential, as staying away from technological advancement can cost more in the long run.