AI in Finance: Seizing Opportunities Amidst Challenges

Artificial intelligence has become a common 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 important ways to use this technology to capitalize the market and be a game changer.

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

We at Rapidops, strongly feel that AI (artificial intelligence) will not just make things easier, it will also help take humanity into an advanced age filled with growth and prosperity.

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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 a great pace. To keep track of this growing domain, you not only require paying close attention but look at it from various perspectives.

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

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

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

We are mentioning a few steps that every organization who 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 want a sound AI/ML algorithm, have a strategy in place for creating a strong data foundation.

Be sure that this foundation has the 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 around trillions of Apache Kafka messages, per day!

Hundreds of petabytes of data get stored across multiple data centres. The sole motive of 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 the near-by 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

Not every business is going this way, as they still think it is an expensive path they are not prepared for.

This is evident in enterprises that are still directing 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 the individual functions.

If the two departments keep looking outside the businesses’ architecture, enterprises are doomed to 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 of 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.

You want a successful implementation of your AI programs, then find out how they are going to 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 for assisting and helping 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 which is 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 the Intel AI, finding hidden patterns in large datasets and transforming them into actionable and explainable information. 

The application of AI in finance sector has seen a number of impressive innovations in recent years. Perhaps the most well-known example is robo-advisors that provide automated, algorithm-based financial planning services with little to no human intervention.

Other examples of AI in finance include:

1. Chatbots that provide customer service or help with banking tasks
2. Predictive analytics that identify financial risks and opportunities
3. Fraud detection systems that utilize machine learning to flag unusual activity

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

1. Smarter credit decisions

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

Artificial Intelligence precisely assess 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 lack nonetheless trustworthy credit history.

Traditional credit scoring systems use rather simple rules to determine an applicant's risk of defaulting. 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 a greater variety of 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 simply 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 their risk management. In addition, they did not experience any deployment delays that are often associated with traditional data science methods.

With vast amounts of processing power, huge quantities of data can be managed quickly. Cognitive computing assists in managing both structured and unstructured data - something that humans are incapable of.

Artificial intelligence is an invaluable tool for financial analysis, making detailed predictions and forecast 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 into 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 take into account a range of different variables. Plaid works as a widget that connects a bank with the client's app, ensuring secure financial transactions.

In the digital age with so many digital platforms, credit card fraud has been increasing rapidly in recent years. With a growing number of people shopping and conducting transactions online the 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 to put a stop to these costly crimes by constantly monitoring clients' behavior, 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 behavior, 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 becomes convenient while managing finances. These apps are handy for:

1. Providing reminders to pay bills on time
2. Helping users plan and track expenses
3. Completing transactions faster

Artificial intelligence does an amazing job of innovating user experience for the banking sector. AI-powered smart chatbots provide seamless and personalized assistance to the clients in a comprehensive manner, hence reducing customer support workload.

Virtual assistants powered by smart technology like Amazon's Alexa are rapidly gaining popularity. This is no surprise, as they boast a self-education feature that makes them smarter every day. 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. These intelligent system track:

1. Income
2. Essential recurring expenses
3. Spending habits

This self-learning helps them create optimized plans and financial tips for the banks users in the most efficient manner. You may also like to read our Banking of Things article.

What does the future hold?

For laying a responsible AI foundation in their business stream, businesses need technology which will complement the entire architecture. Not to forget they will need to focus on the 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 their efforts of creating the architecture, looking for the talent pool and allocation of the resources. We mean to say that unless you embrace machine intelligence with an ethical and responsible dimension of AI, all the above efforts will go in vain.

Highly autonomous AI systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation. – brookings.edu

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

We strongly believe that modern organizations must focus the ethics upfront in your business strategy and decide whether a particular problem needs to be solved through an 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 application of AI.

We are sharing some of the items that businesses can use for applying AI in your business strategy.

1. Formulating an ethical for the AI strategies

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

1. Reduced costs
2. Increased revenue
3. Improved customer service
4. 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

Business must act on 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

As AI becomes a common phenomenon powered by advanced machine learning, the ethical concerns keep growing around it. Companies must develop AI applications by interweaving the ethical architecture.

Provide an 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:

1. Trust
2. Transparency
3. Ethics
4. 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. Find 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 the offerings of artificial intelligence 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 towards delivering your digital product to you.

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

Do you need help for 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 the scratch with our experts.