Artificial Intelligence in Finance: Opportunities Over Obstacles

Rapidops, Inc.
9 min readFeb 2, 2023

Artificial intelligence has become a standard instrument in the success stories of many industries. Although the world has only seen the negative side of AI or technology in movies/series such as Eagle Eye, Terminator, and Black Mirror, the said domain is not focused on ending the human species.

It seems as though no industry or sector has remained untouched by the booming impact and prevalence of artificial intelligence. The world of financing and banking is finding essential ways to use this technology to capitalize on the market and be a game changer.

Artificial intelligence has helped many financial institutions’ processes become more efficient by automating recurrent tasks, enhancing the customer service experience with chatbots' help, and improving the overall operational bottom line.

We at Rapidops strongly feel that AI (artificial intelligence) will make things easier and help take humanity into an advanced age filled with growth and prosperity.

The state of AI (artificial intelligence)

Before expanding the topic and breaking down the benefits of inculcating artificial intelligence into your organization, we would like to get some things straight first.

Let’s begin by gauging the growth and future promises AI (artificial intelligence) holds.

AI is a growing domain, and it’s doing so at an incredible pace. To keep track of this growing domain, you need to pay close attention and look at it from various perspectives.

To lay a strong AI (artificial intelligence) foundation, you require specific command and discipline that many companies still need to understand. And they are not to be blamed. Such a discipline requires strategic collaboration, key metrics, and the necessary talent for working with AI.

Let’s see how you can implement AI in your organization.

  • Identify your goals
  • Find out about the underlying drivers
  • Will AI make your organization more competitive?
  • Is it going to deliver better business value?

We are mentioning a few steps that every organization that wants to include AI in their process must follow.

1. Restructure the data foundation

Big Data is said to be the founding father of AI. Yet the approach towards both is way opposite. If you want a sound AI/ML algorithm, have a strategy in place for creating a solid data foundation.

Be sure that this foundation has computing, storage and analytical capabilities. What’s more important is a shift in perspective.

If you have been using data to track and measure how your business functions perform, shun them. Your current data foundation must learn how to perform these functions with this data.

For instance, UBER!

What this ride-hailing company does with its collected data is fascinating.

Everything Uber does revolves around its data.

The data foundation processes trillions of Apache Kafka messages per day!

Hundreds of petabytes of data get stored across multiple data centres. The sole motive for doing so is to support millions of weekly analytical queries.

For deriving autonomous decisions, Uber uses their in-house system, Michelangelo. This system discovers and manages metadata and ontology. This process is necessary for deriving data-driven performance.

This helps users get a ride to their destination with nearby drivers. Not just startups, established businesses have also realized the use of data for performing better.

For instance, let’s take a look at farm-equipment maker John Deere.

Being a 182-year-old company, they created an open platform for small agricultural start-ups for small-sized businesses to leverage data analysis.

Artificial intelligence has made its way into business architecture. Inculcating data strategy in your business process is a good idea for you. Creating an AI and ML (machine learning) backbone supported by a solid data analytics foundation will help you scale your business.

2. Creating a collaboration that bridges business function with IT

Only some businesses are going this way, as they still think it is an expensive path they are unprepared for.

Enterprises still direct their data and analytical reporting to IT teams. IT drives data and analytics modernization within its own smaller spheres. Analytics teams, on the other hand, focus on individual functions.

If the two departments keep looking outside the businesses’ architecture, enterprises will experience operational inefficiency. We suggest developing an integrated data foundation that can ease enterprise-wide AI adoption.

3. Regular examination of data quality for measuring success

Is your data ready to support the organizational goals and desired business outcomes?

If the organizational goal revolves around generating an AI-driven recommendation for helping users decide when is the right time to invest a sum of their earnings in the stock market, then the data for training and testing the AI system must be high quality. In addition, it must also be highly correlated to the outcomes without any system errors.

4. Assign the correct talent pool to your AI projects

You must hire a talent pool that has better business knowledge. The design-thinking and outcome-driven approaches are necessary for successful data and AI implementation.

If you want a successful implementation of your AI programs, then find out how they will impact the business. You can bring in the head of sales or marketing as the CIO (chief information officer)/CDO (chief development officer)/CAO (chief administrative officer).

And you can always hire a CTO (chief technology officer) to work beside the CDO/CAO. They are needed to assist and help them make the right technology choices.

Applying artificial intelligence in finance

Artificial intelligence is fundamentally changing the physics of financial services.

AI is rapidly becoming integral to FSI businesses, and those that don’t update their infrastructure to support it risk being left behind.

The range of AI tools available to help financial businesses to update their operations is steadily growing. Take Intel® Saffron™ AI, for example. It is an AI-based platform capable of simulating our (human beings') natural ability to learn, remember and reason. This capacity is based on associative memory reasoning technology.

How you choose to run the AI programs and where the technological investments and budgets must be assigned is depended on the CTO.

65% of senior financial management expects positive changes after implementation of AI in finance. — Forbes

With Intel AI, finding hidden patterns in large datasets transforms them into actionable and explainable information.

The application of AI in the finance sector has seen a number of impressive innovations in recent years. Perhaps the most well-known example is robo-advisors which provide automated, algorithm-based financial planning services with little human intervention.

Other examples of AI in finance include

  • chatbots that provide customer service or help with banking tasks
  • predictive analytics that identifies financial risks and opportunities
  • fraud detection systems that utilize machine learning to unusual flag activity

Let’s see how financial institutions can apply artificial intelligence in their business processes to gain a better edge.

1. Smarter credit decisions

ZestFinance successfully cut its losses by 23% annually after bringing AI on-board. — Source

Artificial Intelligence precisely assesses a potential borrower while including a wider variety of factors resulting in smarter data-based decisions. Lenders who utilize AI in their underwriting process identify high-risk applicants and those who need a trustworthy credit history.

Traditional credit scoring systems use rather simple rules to determine an applicant’s default risk. In contrast, AI-based credit scoring relies on far more complex and nuanced rules to make this determination. This enhanced accuracy is possible because AI can consider more variables than humans could hope to process.

The objectivity of machines is yet another perk for banks and other financial institutions. Rather than being inclined to one side, like humans are wont to do, machines provide the facts. Digital banks and loan apps use machine learning algorithms to analyze smartphone data and grant loans or calculate eligibility rates.

2. Better risk assessment and management

US leasing company Crest Financial utilized artificial intelligence on the Amazon Web Services platform and improved its risk management. In addition, they did not experience any deployment delays often associated with traditional data science methods.

With vast processing power, massive amounts of data can be managed quickly. Cognitive computing assists in managing structured and unstructured data — something humans are incapable of.

Artificial intelligence is an invaluable tool for financial analysis, making detailed predictions and forecasts based on different real-time market variables.

Machine learning algorithms evaluate past risk cases and identify early indications of problems that may occur with a potential borrower. This results in better fiscal risk management and assessment with the help of artificial intelligence.

3. Meticulous fraud prevention

Aggregators like Plaid work with some of the biggest names in finance, including CITI, Goldman Sachs and American Express. Their fraud-detection algorithms are complex, constantly updated and able to account for various variables. Plaid is a widget that connects a bank with the client’s app, ensuring secure financial transactions.

With so many digital platforms, credit card fraud has been increasing rapidly in the digital age in recent years. With a growing number of people shopping and conducting transactions online, frauds are bound to increase. However, AI is very good at stopping this type of crime.

Fraudulent activities disrupt businesses and commonly go undetected until it’s too late. AI-based fraud detection systems help stop these costly crimes by monitoring clients’ behaviour, location, and buying habits for any red flags or irregularities.

Another way banks use artificial intelligence is to prevent and uncover money laundering. Machine learning algorithms can detect possible fraudulent behaviour, thereby saving organizations money that would be spent investigating these cases.

4. Up, close and personalized AI banking

Top US banks like Wells Fargo, Bank of America and Chase have developed mobile banking apps. The move makes their clients’ lives become convenient while managing finances. These apps are handy in

  • providing reminders to pay bills on time
  • helping users plan and track expenses
  • completing transactions faster

Artificial intelligence does a fantastic job of innovating user experience for the banking sector. AI-powered intelligent chatbots comprehensively provide seamless and personalized assistance to clients, reducing customer support workload.

Virtual assistants powered by intelligent technology like Amazon’s Alexa are rapidly gaining popularity. This is no surprise, as they boast a self-education feature that makes them more innovative daily. So, you can expect tremendous improvements in this area.

There are a number of apps available that can offer personalized financial advice and help individuals achieve their goals. This intelligent system track

  • income
  • essential recurring expenses
  • spending habits

This self-learning helps them efficiently create optimized plans and financial tips for the bank users.

You may also like to read our Banking of Things article.

What does the future hold?

To lay a responsible AI foundation in their business stream, businesses need a technology that will complement the entire architecture. Remember, they must focus on governance driven by ethics and trust.

Artificial intelligence applications will save banks and financial institutions $447 billion by 2023. — Business Insider

Doing so will help them create the architecture, look for the talent pool and allocate the resources. Unless you embrace machine intelligence with an ethical and responsible dimension of AI, all the above efforts will fail.

“Highly autonomous AI systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation.” —

Many organizations have policies and procedures for identifying and addressing all the ethical considerations once the system is launched.

Modern organizations must focus the ethics upfront in their business strategy and decide whether a particular problem needs to be solved through AI.

Modern business executives underestimate the challenging ethical questions when the emergence of AI becomes sophisticated in its use.

Businesses must act on several fronts if they want to gain benefits from the application of AI.

We are sharing some of the items businesses can use to apply AI in their business strategy.

1. Formulating an ethical for the AI strategies

If you are looking for long-term growth, focus your resources on opportunities that drive measurable value like

  • Reduced costs
  • Increased revenue
  • Improved customer service
  • Enhanced employee experience

Your strategies must have a human-centric view of AI. This will help machine learning work successfully alongside people and benefit your business.

2. Create a governance architecture

Businesses must act to enduring that AI decision-making is transparent. AI must stay free from any data bias and human error. You must personalize the AI so that it can provide tailored and relevant support to those who interact with it.

3. Create applications with responsible AI

Ethical concerns keep growing as AI becomes a common phenomenon powered by advanced machine learning. Companies must develop AI applications by interweaving ethical architecture.

Provide oversight so that these AI systems operate ethically over time, learning and evolving with the help of machine learning.

There are non-technical angles that play critical and complex roles.

  • Trust
  • Transparency
  • Ethics
  • Human-centric approaches

These crucial points must be considered for developing and running the technology. AI and ML have become part of the real business world now. Finding a solution for these technologies to co-exist with business objectives needs to be the top priority for businesses.

Concluding thoughts: Artificial intelligence in finance provides a lucrative growth platform

We hope this article helped you understand artificial intelligence offerings in the finance sector and full-fledged growth. Rapidops is one of the few companies with end-to-end capabilities for turning ideas into impactful products. Our team is dedicated to delivering your digital product to you.

We are dedicated to providing better technical support to boost business growth. We always ensure that clients working with us enjoy a wow user experience.

Do you need help with your next AI project? Or are you looking for a digital product partner? Connect with our team now. Get a free consultation on designing your next AI project from scratch with our experts.

This article was originally published at



Rapidops, Inc.

Rapidops is a product design, development & analytics consultancy. Follow us for insights on web, mobile, data, cloud, IoT. Website: