FinanceTechnology

How is AI Driving Scalability in the Ever-changing Financial Industry?

4 Mins read

Last month, we talked about the benefits of using AI as a business owners. Some industries, such as finance, have particularly benefited from it.

Artificial intelligence (AI) has been transforming various business models in the global financial services industry. It is changing how businesses generate and use insights from data. The changes it has created have been massive. It has led to innovative new business models and reshaped workforces. However, it has also posed some new challenges that decisionmakers in the financial sector have been forced to grapple with.

AI and fintech
Shutterstock Licensed Photo – By Fit Ztudio | stock photo ID: 1513417244

Many companies still err on the side of caution, fearing how challenging implementing AI may be in terms of costs and the time involved. At this time, most financial institutions are mainly using AI to enhance their existing products and services although FinTechs are using AI to come up with new value propositions.

What is artificial intelligence?

Artificial Intelligence consists of technologies enabled by adaptive predictive power that show a degree of autonomous learning. It can dramatically enhance our ability to recognize patterns, anticipate future events, create good rules and make better decisions. This is why using AI is one of the best ways to use technology to grow your business.

AI is rapidly changing what it takes to build a successful business in financial services. Great opportunities lie ahead for financial services using artificial intelligence and machine learning. Some of the benefits include being able to leverage intelligence to define investments for customers tied to their personal financial goals and to use machine learning to significantly improve operational efficiency.

The challenge of access to data and data quality

As companies adopt AI, they face a number of similar challenges. One of these challenges is access to data and the quality of data.

Using an in-memory data grid offers a way for financial services to obtain a high level of performance and flexibility. The distributed design means it is possible to add servers if necessary, to allow scalability and processing of larger transaction volumes.

Data distribution and storage occurs on multiple servers and data is stored in the main memory (RAM) of the servers. As data moves closer to an application, businesses can get fast, low-latency access to critical, accurate information in real-time. Performance is consistent, even during times of peak activity or during spikes in traffic.

The challenge of managing talent needs

Another challenge facing the industry is the fact that the knowledge and skills required today are very different from those necessary to stay competitive in the years ahead. Companies will have to decide what they need to do differently to manage their talent needs and assess what kind of talent they require for new business models. Then it is up to them to prioritize efforts to reskill their workforces.

1. AI improves customer service

In the banking sector, AI is powering smart chatbots that offer clients self-help solutions and reduce the pressure on call centers. Voice-controlled virtual assistants get smarter every day thanks to the data that informs them and can check balances, look up account activity, schedule payments and more.

Large banks have launched mobile banking apps that allow clients to interact with banks in a more streamlined, easier way. They can do anything from completing transactions to getting information.

For applications to run seamlessly and offer consistent service, an in-memory data grid is flexible enough to meet required response times. It can provide an intermediate layer between an application and a relational database and allow frequent, fast access.

Financial institutions that procrastinate in creating new ways to serve their customers and differentiate products will face a battle to preserve margins. Granular insight into customer behavior is critically important in order to offer customers ways to improve their day-to-day lives. They need to use AI if they want to offer customers personalized advice, such as how to manage debt.

2. AI enhances productivity and lower costs

Robotic process automation is allowing financial institutions to boost productivity and cut operational costs. AI-enabled software automates many mundane, time-consuming tasks. It eliminates human error and the attention of the workforce can be on the processes that do need human involvement. Ernst & Young, for example, reported a 50%-70% reduction in costs when using AI for these kinds of tasks.

3. AI offers effective risk management

Using AI in financial services can have a great impact when it comes to risk management. Being able to manage both structured and unstructured data is an impossible task for humans but not for AI. Algorithms can analyze historically risky cases in order to identify potential future risks. By analyzing activities in real-time, it is possible to make accurate predictions and provide detailed forecasts based on many variables.

4. AI assesses potential borrowers accurately

AI enables assessment of potential borrowers faster, more accurately and at less cost. Credit scoring using AI is based on complex rules and leads to better decision-making. Lenders can distinguish between applicants with a high risk of defaulting and those who may be creditworthy but don’t have an extensive credit history. Objectivity is another benefit of a mechanism powered by AI.

5. AI helps prevent fraud

AI is proving to be effective in preventing fraud, particularly credit card fraud, which has increased significantly with so many online transactions taking place. Fraud detection systems are able to analyze customer behavior, buying patterns and location, triggering alerts when anything contradicts established spending patterns. Banks are also using AI to detect money laundering, reducing investigative costs and workload.

6. AI assists with trading

Intelligent Trading Systems can monitor structured and unstructured data very quickly and in trading, faster processing means being able to make quick decisions.

Predictions for stock performance are more accurate when using AI because algorithms can use past data to make predictions. It can put together recommendations for a strong portfolio for an investor and even manage entire portfolios for financial institutions. Bloomberg offers a price-forecasting application for investors called Alpaca Forecast AI Prediction Matrix.

Conclusion

Artificial intelligence is reshaping the building blocks of what it takes to be successful in financial services. It is causing a great upheaval of resources, capabilities, relationships and potential.

Besides making operations more efficient, AI is making highly personalized interactions and customized products possible. It is improving customer service which in turn is helping with customer retention. The center of gravity is shifting and the choices stakeholders make today will affect how successfully financial services operate in the future.

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Annie is a passionate writer and serial entrepreneur. She embraces ecommerce opportunities that go beyond profit, giving back to non-profits with a portion of the revenue she generates. She is significantly more productive when she has a cause that reaches beyond her pocketbook.
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