Predictive Analytics, Customized Solutions, Response Modeling, Churn Analytics are some of the Benefits of Artificial Intelligence in Banking, Says Deltec Bank – Bahamas (2024)

Deltec Bank – Innovation

Deltec Bank – Banks can use automated segmentation to group leads by their interests and use predictive analytics models, such as response modeling and churn analysis

Financial services are attempting to use Artificial intelligence (AI), machine learning, and predictive analytics to reshape the competitive landscape. Hoping to capitalize on the wealth of customer and product data they possess, institutions wish to gain greater market share and achieve new heights. A 2018 Forbes study found that 65% of senior financial managers expect positive changes from the use of AI in financial services. Here are some areas where these goals are being put into effect.

New Customer Acquisition

Banks can use automated segmentation to group leads by their interests and use predictive analytics models, such as response modeling and churn analysis, to determine which marketing content would be most relevant to each of these groups, allowing for a higher degree of personalization.

According to John Young of Hitachi Solutions, “thanks to predictive analytics in banking, sales teams can use machine learning algorithms to automatically evaluate prospects and prioritize leads based on their likelihood to take action, saving precious time and improving the accuracy of lead qualification.”

Predictive Analytics, Customized Solutions, Response Modeling, Churn Analytics are some of the Benefits of Artificial Intelligence in Banking, Says Deltec Bank – Bahamas (2)

Customer Understanding

According to Deltec Bank, Bahamas, “Customers of today want their bank to understand their current needs as well as future wants, and goals. This happens by simultaneously presenting them with personalized products and services.” Customers also want their banks to advise and educate them but prefer making choices based on a variety of options. Finally, they want to be engaged in real-time, with predictive alerts and tailored communication.

All of these can be done by developing a customer profile. But according to Phillip Dudovicz of Hitachi Solutions, “Basic segmentation is a good entry customer segmentation strategy, but you need to take things a step further and work to understand your customers as individuals. It’s a challenge almost every financial institution faces, regardless of whether it specializes in consumer, commercial, or retail banking. Even organizations that excel at collecting really granular consumer data fail to leverage it as successfully as they could.”

Many businesses are falling short on delivering the correct message. A Kony research project found that “the most important customer experience outcome from the consumer’s perspective is investing to make their data more secure.” Kony found that 31% of businesses investing heavily in data security, while only 15% of their consumers perceive this to be the case. Their messaging needs to be improved. Marc Andrews, VP Watson Financial Services Solutions, IBM also found that “Banks that present more targeted solutions to their customers have seen significant improvement in response rates, which has resulted in increases in average deposit balances and reduced attrition.”

Customer churn can be predicted as well. In an article, Wayne State University researchers found that by analyzing a customer’s account activity and combining this information with internal branch and online data, as well as external social media data, it was possible to determine whether or not the customer will leave the bank. They found that certain combinations of signals such as no account activity is registered for a while, the customer does not visit the website nor go to the branch, and they follow other bank(s) on social media, etc. it was possible to predict with great accuracy when the customer will leave the bank.

Fraud Detection and Money Laundering

Fraud detection and prevention are two of the most important issues for banks at the moment and, for this reason, they are the first projects to have been addressed with data analytics and machine learning. Arthur Bachinskiy COO at Fintech solutions company Django Stars, says that “AI is especially effective at preventing credit card fraud. Fraud detection systems analyze clients’ behavior, location, and buying habits and trigger a security mechanism when something seems out of order and contradict the established spending pattern.”

Money Laundering is also prevented and investigated with AI. Bachinskiy added that “Machines recognize suspicious activity and help to cut the costs of investigating the alleged money-laundering schemes.” An Amazon AWS study found that the Anti Money Laundering(AML) investigative workload could be reduced by 20% using AI.

ATM Placements and Funding

A study in the data science journal found that two successful applications of data were to choose where to place ATMs, as well as deciding the optimized amount of cash to load into the machines for weekends. Using city area data looking at where a bank’s customers spend the most, as well as where they are withdrawing money from competitor’s ATMs The bank was able to best position the placement of new ATMs. Additionally, by analyzing the calendar, the ATM’s location, the weather conditions, and the city’s events, the researchers found, “it is possible to make an accurate calculation of the right amount to add to ATMs.”, preventing both overfunding as well as underfunding of the machines.

Conclusion

The correct application of AI in banking is paramount going forward The Kony study above found that, “despite nearly $5 trillion in overall (IT) investment, only 19% of consumers report any significant improvement in the experiences received. Is it a financial institution’s own responsibility to first deliver the products and services customers want, using the AI and analytics tools at their disposal, and then make sure that their customers see the improvements as personalized benefits. This way both sides win.

Disclaimer: The author of this text, Robin Trehan, has an undergraduate degree in Economics, Masters in international business and finance, and an MBA in electronic business. Trehan is Senior VP at Deltec International www.deltecbank.com. The views, thoughts, and opinions expressed in this text are solely the views of the author, and not necessarily reflecting the views of Deltec International Group, its subsidiaries, and/or employees.

About Deltec Bank

Headquartered in The Bahamas, Deltec is an independent financial services group that delivers bespoke solutions to meet clients’ unique needs. The Deltec group of companies includes Deltec Bank & Trust Limited, Deltec Fund Services Limited, and Deltec Investment Advisers Limited, Deltec Securities Ltd. and Long Cay Captive Management.

Media Contact
Company Name: Deltec International Group
Contact Person: Media Manager
Email: Send Email
Phone: 242 302 4100
Country: Bahamas
Website: https://www.deltecbank.com/

Predictive Analytics, Customized Solutions, Response Modeling, Churn Analytics are some of the Benefits of Artificial Intelligence in Banking, Says Deltec Bank – Bahamas (3)

Predictive Analytics, Customized Solutions, Response Modeling, Churn Analytics are some of the Benefits of Artificial Intelligence in Banking, Says Deltec Bank – Bahamas (2024)

FAQs

What are the benefits of AI in predictive analytics? ›

Predicting customer behavior: AI can analyze customer data to predict future behaviors, such as purchasing patterns, product preferences, or potential churn. This insight helps businesses tailor their marketing strategies, improve customer engagement, and enhance overall customer experience.

What are the benefits of predictive analytics in banking? ›

Advanced analytics techniques analyze transactional data in real time, identifying suspicious patterns and anomalies to prevent fraud before it occurs. Predictive analytics in banking can monitor customer transactions, flagging unusual spending patterns or account activities indicative of fraudulent behavior.

What are the benefits of artificial intelligence in data analytics? ›

AI technology has many advantages over traditional methods in data analysis. It speeds up processes, reduces costs, and improves decision-making precision. Organizations across sectors like insurance and finance can benefit from AI's capabilities in image recognition, language understanding, and strategy optimization.

What is the difference between predictive modeling and AI? ›

The most glaring difference between AI and predictive analytics is that AI can be autonomous and learn on its own. On the other hand, predictive analytics often relies on human interaction to help query data, identify trends, and test assumptions, though it can also use ML in certain circ*mstances.

What are 4 advantages of AI? ›

The advantages range from streamlining, saving time, eliminating biases, and automating repetitive tasks, just to name a few. The disadvantages are things like costly implementation, potential human job loss, and lack of emotion and creativity.

What are the benefits of predictive data analysis? ›

Predictive analytics gives businesses insight into their existing database; tapping into the available customer data helps you understand why you are losing customers to your competitors. It can also help identify unique selling points to acquire qualified leads.

Why do banks need data analytics? ›

Data analytics has a range of applications in banking. It uses advanced data processing techniques to extract valuable insights from vast financial data, enabling banks to make data-driven decisions, assess credit risk, and detect fraudulent activities.

What is the most important benefit of AI in banking industry? ›

One of the most significant benefits of AI in banking is automating decision-making in underwriting and credit analysis.

What is predictive and prescriptive analytics in banking? ›

Risk Management: Predictive and prescriptive analytics can help financial institutions manage risk more effectively by identifying potential risks and recommending mitigation strategies. This can help reduce losses and protect the institution's reputation for all stakeholders.

What are the benefits of using AI in decision-making? ›

AI and human decision-making provide data-driven insights, enhancing the quality and efficiency of the decision-making process across various business domains. Its ability to process, analyze, and derive insights from vast datasets significantly influences the accuracy and effectiveness of the decisions made.

What is predictive analytics and modeling? ›

Predictive analytics is the process of using data to forecast future outcomes. The process uses data analysis, machine learning, artificial intelligence, and statistical models to find patterns that might predict future behavior.

What is predictive modelling used for? ›

Predictive modeling is a mathematical process used to predict future events or outcomes by analyzing patterns in a given set of input data. It is a crucial component of predictive analytics, a type of data analytics which uses current and historical data to forecast activity, behavior and trends.

What is an example of predictive analytics in real life? ›

Real-life example of predictive analytics in supply chain

Amazon uses predictive analytics to master its supply chain management. By scrutinizing historical sales, customer behaviors, and external influences like seasonality, they adeptly forecast product demand.

What function does AI service in predictive analytics? ›

Predictive analytics is a core function of enterprise AI. The foundational purpose of enterprise AI applications is to serve as “prediction engines,” providing insights to drive actions that improve business operations and performance.

What are the benefits of AI forecasting? ›

Benefits of AI Demand Forecasting

AI demand forecasting can help your business automate workflows, achieve better accuracy, limit your risk, and improve customer satisfaction.

How does AI help in predictive maintenance? ›

Predictive maintenance using AI can prevent equipment failures by predicting when a maintenance action is required. Rule-based systems use hard-coded thresholds, while ML-based systems use advanced modelling to predict failure. Anomaly detection in rotating machinery is a key step in AI-based predictive maintenance.

What are 3 major benefits of using AI in software testing? ›

Conclusion: In conclusion, the integration of AI in software testing offers significant benefits, including increased test coverage, faster time-to-market, enhanced accuracy and reliability, optimized resource utilization, and continuous improvement through feedback.

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