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Over the next few years, three of the four largest accounting firms have committed to investing $9 billion in artificial intelligence (AI) and data analytics products and training. Artificial Intelligence (AI) is altering the way we do business. We have it all, from data-driven decision-making to autonomous operations.

All of this is filtering down to businesses that rely heavily on AI, which brings us to business decision-making. With data-driven decision-making, AI has a significant impact in this area.

"Around 70% of businesses will use at least one type of AI technology by 2030, and roughly half of all large businesses will be having a full range of AI technology embedded in their processes." - McKinsey predicts.

AI In Finance

Artificial intelligence is changing the way we interact with money in finance. From credit decisions to quantitative trading and financial risk management, AI assists the financial industry in streamlining and optimizing processes.

AI is used by businesses to gain a competitive advantage:

• They can make more data-driven decisions.
• They automate some repetitive tasks that AI can complete much more quickly than a human employee could.
• They can increase their profits directly through effective targeting or precise recommendations.
• They reduce churn by identifying "hesitating" customers early on.

The financial industry is being affected by the same "AI revolution."

"70 percent of all financial services firms are using machine learning to predict cash flow events, fine-tune credit scores, and detect fraud." - According to Forbes.

AI In Decision Making

Due to a lack of time, facts, or the ability to quickly access data, AI can help you gather the data you need to make the best decision possible. We can't be sure about everything, but AI has a far greater capacity than humans for rummaging through all the data at its disposal.

An organization will need time to prepare its physical assets, people, information technology, processes, and products and services to operate at digital speed. This includes consistently generating accurate digital information, absorbing, and using digital information effectively, and implementing adopted changes quickly.

AI can make some decisions and carry out the chosen operational actions independently. For example, today's AI technologies can autonomously run a manufacturing line. Human intervention is still the final determinant of the effort to be taken in many cases.

In some cases, the technology exists to eliminate human involvement. Still, incidents like the one involving the Boeing 737 Max, in which software overrode pilot inputs and sent the planes into a nosedive, highlight the challenges that remain before fully autonomous or near-autonomous AI is widely accepted.

Financial Use Cases of AI-Driven Decision Intelligence

For most financial institutions, implementing advanced analytics is a challenge. However, those who persevere see excellent results, particularly in the following niches.

Management of Assets and Investments

This field is incredibly ripe for decision intelligence adoption! The following are the most critical use cases to investigate:

• Weather forecasts, online company sentiment, media coverage, and other alternative data for investment decisions are being analyzed to improve hedging strategies.
• Automated insights on individual customers' portfolios are available in real-time.
• Intelligent client outreach based on recent online and in-person behavior patterns

Morgan Stanley WealthDesk is an excellent example of how decision intelligence can help you make better decisions. Morgan Stanley advisors can use the WealthDesk platform to run advanced scenario analysis for their clients in real-time and recommend various viable investment strategies.

Payments

Decision analytics can be a game-changer for customer security for banks and payment service providers. Replace outdated rule-based systems with adaptive measures that adjust security levels in real-time based on customer behavior.

Such systems can help you prevent fraud and improve your customer relationships.

Did you know that 73 percent of shoppers with an annual income of $800,000 to $999,999 had their card declined at least once while shopping online last year? This contrasts with a 30% average across all customer segments. Their banks are losing points because of this.

• Decision intelligence can help you balance top financial security and excellent customer service. Take, for example, Mastercard's Decision Intelligence solution. It collects a customer's debit and credit card data in real-time then analyses various data points associated with a transaction using machine learning algorithms to determine whether it's legitimate or fraudulent.
• Intelligent decision models can perform automated due diligence for large transfers (including cross-border transfers) to speed up clearance. The "three-day good funds model" is no longer sufficient for most customers.

Retail Banking

Advanced analytics can also benefit retail banks, especially considering the increased competition from digital banks.

Here are some of the most significant value opportunities:

• Effective Pricing Strategies: One US bank used machine learning to analyze the discounts offered by private bankers to their customers. What they discovered was that their bankers gave out too many unnecessary discounts. After correcting the strategy, the bank's revenue increased significantly within a few months.
• Data-driven product marketing: Lloyds Banking Group attributed 24 percent of new leads to the latest analytics solution after deploying a group-wide analytical ecosystem, allowing the bank to market their products with greater precision at the right price.

How Can AI Help Business Intelligence Applications?

Business intelligence is defined as a set of procedures for collecting, processing, and analyzing large amounts of data. Enterprises use various business intelligence tools for this, including Tableau, Datapine, Zoho Analytics, and others.

Some issues may arise during this process, reducing the system's value to businesses. A large volume of data, for example, can raise a capacity limit that must be pushed back.

Let's look at how AI can help with flaws.

AI Boosts Functionality of Business Intelligence

The true potential of business intelligence can be seen when a large volume of data is broken down into granular insights. It enables businesses to comprehend the finer points of a larger picture. AI also enhances business intelligence applications' capacity and functionality. However, the real issue can arise in real-time insight.

The primary goal of BI is to process and visualize data. But! BI cannot generate this data result and trend prediction in real-time. When combined with cutting-edge technologies such as machine learning (ML), AI can generate real-time insights and trends.

Increasing BI functionality increases the organization's value.

Bridge the Gap

BI-enabled businesses benefit from BI because it provides critical insight into data they previously couldn't examine. AI-enabled BI applications process new data and identify trends for businesses.

Machine learning, predictive analytics, and natural language processing are examples of cutting-edge technologies that provide valuable insights. Only visualizing data isn't enough for businesses to see trends; they need tools to bridge the gap, which AI can provide.

Simplifying Complex Processes

Even with a BI solution, data surveying is a difficult task. A professional data analyst must examine hundreds of charts to gain actionable insights. On the other hand, AI can make this process easier by using deep learning, machine learning, and natural language processing. Machine learning enables AI technology to understand human language, making it easier for data analytics to find connections and insights.

Furthermore, AI-enabled BI allows companies to handle and process large amounts of structured and unstructured data.

Get Rid of Talent Storage Issues

Business intelligence offers data findings in a visual format by processing data from various sources. Even in a visual form, data representation can be challenging to read. Artificial intelligence, on the other hand, can define and scale data at the most basic level, allowing businesses to gain actionable insights.

Furthermore, talent issues can inhibit the processes. Several AI development companies are using the technology in business intelligence to solve these issues. The suitable software processing can be used with AI to eliminate problems caused by a talent shortage; data analysts can easily delegate tasks.

Can Artificial Intelligence Make Better Decisions Than Humans?

When data is collected and analyzed correctly, it can provide decision-makers with deep, unparalleled insight into every aspect of a business.

The issue that modern businesses face is that they are drowning in data. Humans are unable to keep up. As a result, companies must choose between keeping humans in charge and allowing machines to take over some data-driven decision-making.

Humans will always be limited when making data-driven decisions. According to the Harvard Business Review, this is because:

• Humans Don't Leverage All the Data: We struggle to gather all aspects of a dataset, such as insights, relationships, and patterns. Humans are prone to summarize large amounts of data, and we are physically incapable of processing millions (or billions) of records. Our minds struggle to connect the dots between data elements, critical for sound decision-making.
• Humans allow our biases to take control: We prioritize data based on what we believe is essential rather than what the data says. This is because humans think of data in linear terms, and our brains are incapable of processing aggregates in the same way that AI can. AI can quickly gather the information required and cut through the noise that would otherwise cloud a human judgment.

And AI does not have feelings: it can make decisions and collect data without being influenced by emotion, bias, or human error. And, as unbelievable as it may seem, AI can now outperform us in everything from video games to image recognition to lip reading.

If you like bold and science fiction-sounding statements, you might be interested to know that some experts now predict that AI will outperform humans in all areas by 2060. Many people believe that AI will be able to outperform humans in tasks such as translating, writing essays, driving, and performing surgeries in the coming years.

Conclusion 

Companies must integrate Artificial Intelligence (AI) into workflows and, in some cases, get humans out of the way to fully leverage the value contained in data. We need to move away from data-driven workflows and toward AI-driven workflows.

This isn't just an automation play, a side effect of incorporating AI into decision-making. Instead, it enables us to overcome our inherent limitations as human processors–low output and cognitive bias–by delegating data processing to machines and applying judgment, culture, value, and context to the decision options that devices can generate.