13 Digital Transformation Opportunities for Financial Services

Financial institutions may be a part of everyday life in the United States but recruiting and retaining customers isn’t easy. Satisfactory customer experience and customer trust are crucial to banking, investing, and loan services, especially when there are so many competitors nationwide.

Bringing relevant services and products to clients’ attention requires careful incorporation and analysis of existing company data and overall market trends. Some services, like mobile applications, are increasingly mandatory for financial institutions to offer.

There is also huge potential in big data and analytics for financial services, especially when considering their applications in marketing. Computer programming and processing power have improved exponentially in recent years, making it possible for artificial intelligence to make predictions around client behavior and preferences.

Here are 13 key ways financial services companies can transform themselves in the digital age.

1. Make Forms Fully Automated

Many online forms still require human review. Everything from mortgage paperwork to credit card applications usually passes under human eyes at some point in the process. This usually takes at least 24 hours to process, leading to decreased convenience for clients.

Most forms can be machine-read and checked with a list of acceptable values, then approved or declined. A few forms may still need to be manually reviewed, but advanced artificial intelligence can learn from the results of that review and implement that logic when reviewing similar forms in the future.

2. Mobile Apps

Younger and older generations are becoming more mobile and reliant on smartphones. Financial services apps provide a powerful tool for on-the-go professionals who need easy access to their checking accounts and credit cards. With the rise of mobile check deposits, clients are spending less and less time in brick-and-mortar banks.

Even stock investments and mortgages can be securely managed via mobile app. In order to remain competitive, financial services companies must provide convenient apps for all services, while keeping desktop and in-person services available for clients who prefer them.

3. Marketing Mix Attribution

Marketing mix attribution is the ability to determine which marketing channels are working. While some data can be collected through client surveys and other manual means, efficiently and accurately collecting marketing data requires automatic processes.

Most websites and online advertising platforms can calculate which ads are clicked on by customers, but the influence of print advertising can be difficult to calculate. Big data can help with this by drawing correlations between customer demographics and the likely channel of advertising exposure. For example, data analytics may discover that most clients in a certain zip code found out about a financial service through a particular billboard or subway advertisement.

Falsely flagging a legitimate purchase can result in a major inconvenience for the customer, but ignoring a fraudulent transaction can have financial consequences for the credit card company, retailer, and customer.

Credit card fraud analytics rely on data analysis methods and machine learning that are continuously improving. However, criminals are also growing more sophisticated and resorting to account takeovers to sustain fraudulent activities. Credit card companies must improve their systems and develop risk management plans to detect and reject these takeover attempts in order to reduce losses caused by fraudulent use.

6. Insurance Claim Fraud Detection

Fraudulent insurance claims are a common white-collar crime. It’s easy for individuals to exaggerate or even make up a claim in order to get more money than they’re entitled to. Natural disasters, in particular, can bring about large numbers of fraudulent claims, ranging from inflated contractor costs to misclassified damage.

Machine learning can detect abnormal claims that are likely fraudulent. Insurance companies can use years of data to establish general trends and acceptable thresholds for costs, then develop programs to automatically detect claims that should be investigated further.

7. Automated Wealth Management

As traditional pension plans decline in popularity, consumers are seeking more advanced ways to grow their stock portfolio. Many clients don’t have the time to monitor stock growth, sell stocks that have peaked, and reinvest dividends and sell stock value into new stocks with high growth potential.

Automated wealth management can be tailored to individual clients’ needs. For example, some clients prefer slow, yet steady, portfolio growth, while others may be willing to take larger risks in hopes of larger rewards. AI can be taught to manage client wealth based on these preferences.

8. Meeting Consumer Needs with Big Data

More companies are innovating with big data in financial services. Financial services analytics companies can help financial institutions analyze existing and potential customer data and figure out what new services should be created for them.

For example, a financial institution that primarily serves wealthy individuals may want to branch out into small business services, tapping into current clients’ business ventures. The best options can be determined by careful analysis of big data on existing clients.

9. Reducing Duplicate Forms

Sometimes signing repetitive forms is required by law, such as at a mortgage signing. Other times, the forms are a holdover from antiquated systems that don’t know how to streamline data.

Well-planned data management systems can reduce the amount of time clients spend filling out forms, improving client satisfaction. An easy example of this is a form that auto-fills with the client’s address and phone number on file, with a gentle prompt to double-check the information for accuracy.

10. Cloud Computing

Cloud computing allows different systems to easily access a variety of data without huge investments in servers. Cloud computing providers have advanced security and easy-to-understand options for financial services analytics data storage and processing.

Cloud computing also allows companies to run multiple data analysis programs at once, improving turnaround on marketing developments, and reducing the time required to detect fraud. Moving from physical servers to cloud computing takes minimal effort and can massively improve a company’s computing capacity.

11. Data Lakes

Data can be stored in a wide range of ways inside cloud servers. One of the newest ways to store it is in the form of a data lake, which keeps data in its raw form for storage and analysis.

Managing data lakes requires specialized knowledge from financial services analytics companies, as these lakes are much less organized than data warehouses and need optimization in order to run programs quickly. However, this lack of firm organization also allows it to be sorted and used more fluidly, with fewer restrictions and obstacles for programmers.

12. Augmenting Google Analytics

Financial services industry websites often rely on the Google Analytics suite of services to monitor traffic, incoming links, and more. However, there are strong limits to Google Analytics’ ability to predict the “who” and “why” of web traffic.

Augmenting Google Analytics allows businesses to predict consumer behavior and understand how to meet consumer needs. This can influence everything from future service offerings to website redesigns.

13. Open-Source Migration

Many financial services and other industries rely on proprietary programming languages and other data management tools. However, open-source tools are increasingly a better investment for large and small companies.

Open-source tools are both free and powerful, and the technology services labor pool is full of IT experts with the knowledge to tailor those tools to your needs. Open-source migration is a one-time investment that frees companies from annual licensing fees and proprietary service restrictions.

Preparing for Future Growth

Opportunities come with a learning curve for staff and clients alike. Customer service representatives must be trained on the new features, and clients need step-by-step tutorials for more advanced features. Fortunately, there are industry best practices for designing and implementing new features and internal data system changes.

At Syntelli Solutions, we have experience in data management, machine learning, customer intelligence, and big data strategies for financial services. Whether you’re seeking a huge system overhaul or just need a new data analysis feature, contact us so we can walk you through the next steps.

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