14 Oct Why Financial Services must cash in on the analytics wave
Financial services are under constant pressure to find innovative ways to grow their revenue and assets. Simultaneously, competition is extreme as financial service providers compete for the wallet share of customers. To beat the competition and drive sales, financial services are looking at versatile technology channels like the cloud, advanced analytics, and artificial intelligence to acquire new customers and building more productive and profitable customer relations.
The leading firms in the game including offshoots of HSBC, Citi, Barclays, and others have already started harnessing their ever-growing volumes of data to generate deeper insights, enabling them to acquire and expand their customer base. These companies aremaking sense of the data at hand byleveraging analytics technologies like Deep Learning, Machine Learning, and Artificial Intelligence.
The financial firms data driven analytics need data that they can trust transparently, which help them verify data quality, data provenance, and traceability. They then seem to deliver an improved customer experience and business benefits likerisk-optimization, improvedpayments and collection, and enhanced productivity.
Reasons to use data-driven analytics for financial services
1. Improve the customer experience
Customer experience is now the centerpiece of every service. By implementing data-driven analytics, financial service companies aim to be able to provide a better customer experience in several ways:
· Customized financial service
The companies can gather and use data based on customer satisfaction parameters, buying history, demographic data, preferences, and buyingbehavior. The data can help them provide tailor-made products and services and “personalized” offers. Driven by this data, the firms can recommend the most suitable products for their customers to buy. This ultimately contributes to a more custom customer experience, customer satisfaction, and customer retention. For example, American Express uses Machine Learning for a wide range of interactions to personalize customer offers and streamline sales.
· Automated-advisor services
Automated-advisors, or robo-advisors, were launched in 2008 in the midstof the financial crisis. They are designed to provide financial advice or investment management with minimal human intervention. Companies can implement such automated services to offer digital financial advice driven by mathematical rules and algorithms. Specially designed software executes these algorithms to deliver investment advice just like a human advisor. Such automation can also manage portfolios and provide better investment suggestions by analyzing customer risk profiles. For instance, TransUnion has launched a self-service platform which helps its customers assess their profiles for risks and take better investment decisions.
Much has been written about the use of chatbots already. Suffice it to say that these have now moved beyond the hype and into everyday reality. The financial services data driven sector can take advantage of Artificial Intelligence technology for improved customer experience. AI-based chatbots can help customers to provide details, authenticate, and conduct financial transactions quickly. For customers, the information is available on their fingertips, eliminating the need for human interference. On the other hand, the data captured in such interactions can help banks understand customer’s behavior and their buying decision, allowing them todeliver a more personalized user experience.
2. Fraud protection For Financial Services
Modern financial institutes faceterrifying fraud risks. As per the reports, financial firms had lost more than $2.2 billion in 2016 to fraud and malpractice. To prevent such damage, data-driven analytics can help firms to identify threats through a deep understanding of customer data. Analytics can detect unusual activity and raise red flags when something untoward seems to be taking place.
Of course, financial firms collect versatile data including customer name, email, IP address, phone number, payment method, time of payment, CVV, currency, to name a few. They also build up massive amounts of data on transaction behavior, by specific customers and across categories. This is a critically important treasure trove of data in which the solution to combat fraud lies. Financial firms can use data from the transaction to the aggregate-level to fight fraud.
3. Automate business process
Data-driven analytics promises the transformative power of automation for financial firms. The aim is nothing lessthan total data integrity with zero manual errors.
- Firms can automate key manual tasks including journal entries, accounts reconciliation and can create financial statements with minimum human interference.
- They can streamline financial and transaction process, ensuring smooth operations.
- The data gathering, data transfer, and storage can be automatedand validated by integrating different systems.
Such automation can free up key executives to make strategic decisions which need human intervention, human experience, and intuitive decision-making abilities. Of course, reporting and monitoring processes can also gain from automation, giving executives the opportunity to get a complete picture of their operations at all times.
4. Benefits of predictive analytics in financial services
Creating a robust data-drivenecosystem will help financial services firms predict operational demands through predictive analytics. This predictive capability can help drive great benefits. For eg.,
- Financial firms couldsignificantly reduce customer churn by identifying and remedying key customer churn triggers.
- The credit cards division could increase offer conversion and customer purchasesby leveraging deeper customer buying insights.
- Firms could improve operational efficiency by identifying and eliminating the roots of problematic transactions.
As financial service companies gain value from data-driven analytics, they seem set to unleash innovation and create organizational enthusiasm for using data. These companies could create new revenue opportunities by monetizing existing data assets. Analytics could usher in a new financial services revolution driven by innovation.