Innovation In Financial Services Driven By Emerging Technologies

Shaily Kumar

In the resolutely unpredictable global marketplace, the financial services sector is confronted with a rapidly evolving backdrop, as the industry’s speed of change continues to accelerate. These challenges largely come from innovations in the application of information and communication technology in the sector.

The evolution of emerging technology and changes in market conditions, economic policy, and regulation have created novel opportunities for economic and social growth in the sector.

The financial services sector is faced with a divergence of challenges that stand to hamper its progress, and emerging technologies are being used to combat these complex problems. Two of these challenges that technology is helping to solve are money laundering and mobile payments fraud.

Money laundering

Money laundering involves the concealment of the origins of illegally obtained money, typically using transfers through legitimate banks or commercial transactions. Generally, multiple transactions are used to cover up the source of financial assets so criminals can access them without being discovered.

Money laundering transactions fall into three categories:

  • Placement, which is the process of putting unlawful proceeds into financial institutions through deposits, wire transfers, or other means
  • Layering, the process of separating the enforcement problem
  • Integration, when the money launderer integrates the illicit funds into the economy

Money laundering is a complex problem in the financial services sector. Despite anti-money laundering laws, the problem continues, as the law is evidently weaker than the criminals committing the crime.

Over the past few decades, many banks have used rules-based systems for detecting suspicious activity and generating alerts. But now, emerging technologies enable financial institutions to improve detection by interpreting external structured and unstructured data, mashing external data with internal data, applying machine learning algorithms, and making better use of the data.

Some of these algorithms and techniques are simpler to understand than others. Simpler algorithms include decision trees, and more complex algorithms may comprise neural networks. They help solve the problem of money laundering by looking at strings of intricate customer transactions, interpreting them, and supplying bases for further investigation. Once identified, these suspicious transactions are further investigated in order to track down money laundering criminals.

Mobile payments

Mobile payments, which mobile or portable electronic devices to make payment transactions or transfers, have become very popular. However, cybercriminals are targeting financial institutions through the mobile payment system to get customers’ financial data to carry out fraudulent activities. Financial institutions are working to secure customers’ data and prevent these crimes.

Biometrics is one way they’re trying to solve this problem. It involves is the statistical assessment and analysis of:

  • Physical characteristics, such as facial, fingerprint and iris verification
  • An individual’s behavioral uniqueness, including voice identification, gait, and how they use a keyboard and mouse

Authentication with biometric verification is very common today in part because it provides solutions that are both secure and convenient. Customers can be authenticated into web-based banking services from home or other locations. Other challenges, like operational failures, can be solved with a scalable biometric authentication platform linked to a mobile biometric that can control multiple biometric modalities. Across many channels, biometrics is increasingly used to reduce financial fraud by effectively authenticating customers and authorizing financial transactions.

Transformation is not negotiable

Regulatory demands and competitive forces are requiring the financial service sector to transform into a digital operating model. Survival in today’s financial markets requires bold, innovative strategies to find incremental value in a real, unified, and data-driven world.

Maintaining the status quo would hinder the sector’s effectiveness and efficiency, as no organization can thrive without innovative thinking and approaches. As change remains inevitably constant, the financial services sector needs to think beyond conventional ways of operating to keep up with current trends and demands, avoid disruptions, and achieve in the sector.

Learn more about 5 key topics in addressing the challenges for the banking and financial services industry.


Shaily Kumar

About Shaily Kumar

Shailendra has been on a quest to help organisations make money out of data and has generated an incremental value of over one billion dollars through analytics and cognitive processes. With a global experience of more than two decades, Shailendra has worked with a myriad of Corporations, Consulting Services and Software Companies in various industries like Retail, Telecommunications, Financial Services and Travel - to help them realise incremental value hidden in zettabytes of data. He has published multiple articles in international journals about Analytics and Cognitive Solutions; and recently published “Making Money out of Data” which showcases five business stories from various industries on how successful companies make millions of dollars in incremental value using analytics. Prior to joining SAP, Shailendra was Partner / Analytics & Cognitive Leader, Asia at IBM where he drove the cognitive business across Asia. Before joining IBM, he was the Managing Director and Analytics Lead at Accenture delivering value to its clients across Australia and New Zealand. Coming from the industry, Shailendra held key Executive positions driving analytics at Woolworths and Coles in the past. Please feel to connect on: Linkedin: http://linkedin.com/in/shaily Twitter: https://twitter.com/meisshaily