Today, data is a key driver of business success. But data, to be useful, must be captured, analysed, and put to purpose. Financial institutions grapple with several challenges on this front. 

The proliferation of digital channels and transactions has led to an explosion of data. Traditional data management systems struggle to handle the data deluge. The result is inefficiencies and bottlenecks. These systems are also rigid and resource-intensive. They cannot meet the expectations of modern businesses or their customers.

Proactive credit risk management

The existence of financial institutions depends on proactive risk management. Risk management, however, has become tougher in today’s connected ecosystem. 

In today’s age of high data velocity and instant decisions, effective risk assessments require timely access to accurate data. Traditional data management platforms often process risks based on incomplete or outdated data.

A priority for financial institutions is to collect and analyse data from diverse sources. They collect data from credit bureaus, online transactions, social media, and other sources. Next, they need to build risk models for their business cases. For instance, a lender’s risk model must assess a potential creditor’s repayability. An ideal model would consider the creditor’s credit history, income, and employment status. Applying machine learning algorithms to such data identifies patterns, trends, and potential risks. Other models allow lenders to take proactive action. If, for instance, the customer is going through a financial crunch, the company can adjust credit limits or restructure the loan. 

Innovative measures to combat fraud 

Combating fraudulent activity has always been a challenge for financial institutions. Digitisation has increased the scope, depth and velocity of frauds. Fraudsters employ sophisticated methods such as AI-enabled deep fakes to commit large-scale frauds. The risks of cyber security incidents also remain high.

Financial institutions need new techniques and approaches to deal with new-age frauds and threats. One tool that has become popular is graph databases. Each graph database consists of data elements representing a customer or an account. Visual elements make explicit the connections between these elements. These connections could include identity, transaction, or social connections. An analysis of such connections makes explicit unusual or suspicious patterns. For instance, the graph elements make explicit multiple accounts under different names from the same IP address. The success of graph databases again depends on real-time data.

PayPal’s bespoke graph analysis analyses millions of records within 20 milliseconds to unearth fraud risk. The company uses such real-time insights to apply prevention processes, saving millions in fraud losses.

Focus on personalisation

Today’s customers demand personalised and seamless experiences. No business can expect to survive by continuing the old bureaucratic approach. The customer will no longer come to them or adjust to their processes and ways of work. 

Today’s competitive environment requires businesses to overhaul their processes around the customer. Personalisation is a key enabler of such a customer-oriented approach. Financial institutions can strengthen their personalisation initiatives by analysing customer habits and preferences. They can do so by aggregating real-time data from location-based services and other sources.

Analysing customer-related data enables building an accurate picture of customers’ financial behaviour. Such insights allow tailored product recommendations, pricing, one-to-one loyalty programs, and targeted campaigns. 

Analysis of personalisation data also speeds up customer onboarding and predicts customer churn.

Personalisation enhances the customer experience and creates strong customer relationships. The benefits go beyond an increase in sales per customer. Happier customers spread favourable word of mouth and referrals, leading to a positive growth spiral.

Cultivating data culture

The success of modern data initiatives requires a data-centric culture that empowers employees to use data.

Most enterprises grow organically, and in the process, data management also becomes ad hoc. For instance, when a reporting requirement comes up, the business unit sets up an ad hic system to support such reporting. With little or no central oversight, the unit becomes responsible for security, privacy, master data, and metadata usage. 

Soon, data fiefdoms emerge, where people protect the assets they have built up over time. Such silos become a roadblock for leveraging data to streamline operations or make informed decisions.

The success of enterprise data initiatives depends on moving away from data fiefdoms to collaboration. Financial institutions need to:

• Promote transparency and open data sharing. Encourage collaboration across departments and teams. Make employees feel comfortable discussing data-related challenges and ideas.
• Recognise and reward employees who commit to using data to drive business outcomes. Such rewards may include bonuses or some other forms of recognition.
• Provide education and training for employees to enhance their data literacy skills. Equip them to interpret data, use analytics tools, and make data-driven decisions.
• Champion the importance of data-driven decision-making. Top executives could walk the talk by sharing information and prioritising data initiatives.
• Ensure the employees understand the ethical implications of working with data. The financial industry has especially high standards for privacy and security. 

More robust data quality and governance

The complex business landscape and reliance on data have led to stringent compliance regulations. These regulations relate to data security and responsible use of customer data. Non-compliance risks the enterprise paying hefty fines besides reputational damage.

Strengthening data governance ensures data integrity and meets compliance requirements. A good data governance policy lays down detailed standards that offer a benchmark for data assessments. But managing the new-gen data challenges also requires:

• Centralising data. A central repository provides a single source of truth. It ensures everyone works with consistent information. Document data stores collect information from various sources for analytics.
• Establish frameworks for ongoing data quality surveillance to identify quality and security issues. 
• Deploying automated data validation techniques. This may include running automated scripts for range validations and consistency checks. The scripts may also analyse the content and structure of data to identify anomalies and other quality issues. Automating routine data governance tasks such as profiling and cleansing facilitates data quality.
• Appointing data stewards or custodians with clear roles and responsibilities. Common responsibilities include mitigating data quality discrepancies for the data under their responsibility.
• Setting up comprehensive documentation on regulatory requirements for data handling and protection.

Data governance and culture change often go hand in hand. Financial services often walk a tightrope. They have to balance openness and transparency with the confidentiality of sensitive data. 

Embrace state-of-the-art data management tools

Access to real-time data has become a make-or-break deal for financial companies. Customers now expect a fast and personalised response.

Modernising the data infrastructure is inevitable to process huge quantities of data in near real-time. And this requires a reliable enterprise-grade data platform operating in real-time at a petabyte scale. 

Practical considerations dictate that such a platform be available in hybrid mode and with a low total cost of ownership. The best platform is also cloud-based to offer agility and easy scalability.

State-of-the-art platforms such as Informatica allow enterprises to build their data infrastructure effortlessly. Informatica ensures data transparency and ease of access while maintaining the confidentiality and integrity of sensitive data. Financial institutions can use these tools for advanced analytics and extract actionable insights.

Informatica’s CLAIR AI Engine enables intelligent automation. It automates data management tasks while reducing complexity and accelerating data delivery. It also offers a single view of the data governance process and the underlying technical metadata. Linking these together offers a better relational view of enterprise metadata.