The real trick is being ready for the trip
In 2017, the global banking industry encountered numerous changes. Keeping that in mind, it is prudent to analyze core factors that’ll drive impending industry changes and challenges that banks are likely to face in 2018.
Driving factors to watch out for
- Artificial Intelligence: Banks and other financial services firms will focus on collecting and analyzing data across different sources to understand customer behavior patterns. Machine learning will assist banks in analyzing huge piles of data from different sources to deliver meaningful insights about customers. This will help banks target different customer segments appropriately. Having been segmented by behavior, these customer segments differ from the traditional product-based customer segmentation. Data visualization will be fundamental to identifying customer segments and it’ll be heavily influenced by AI techniques. Banking areas that will get most traction include customer acquisition, product offerings, risk management, credit decision-making, investment advisory services, and customer queries/complaints handling.
- Processes Automation: Automation’s collective impact on banking jobs is already being debated. In 2018, we will experience a huge leap in the Robotic Process Automation (RPA) space. Jobs that are repetitive and prone to human error will be automated. Application processing, reconciliations, issuing periodic letters, offers, customer notices, and generic query handling are most likely to be automated.
- Fintech Capabilities: The word ‘disruption’ will continue to gradually disassociate itself from fintechs. Banks have learned that fintech competencies are a must-have in regard to their overall service offerings. Fintech features have to merge into the offerings blend through partnership, acquisition, or in-house development. In 2018, we’ll see more collaborations and partnerships that will establish bank-fintech amalgamations. Its outcome will be addition of new customer segments to existing customer base, ultimately leading to new revenue streams for banks.
- Open Banking (PSD2): With the legalization of PSD2 regulations in European Union in early 2018, many banks will have to focus on PSD2 compliance. Banks must put in place effective strategies to establish strong customer authentication, as per these guidelines, without impeding customer experience. Initiating a two-factor authentication for every customer transaction may be detrimental to the rich customer experience a bank is planning for. Therefore, banks will have to focus on exemptions to ‘strong customer authentication’ (SCA, a term under PSD2) and judiciously build their services around them.
PSD2 will not be limited to European Union; we’ll see its propagation in other parts of the world in 2018. Newer API (application programming interfaces) development and its monetization will drive the open banking success story.
While banks continue to progress in different directions, they might encounter challenges along the way. Some of anticipated challenges are:
- Start-up Mindset: Artificial Intelligence and blockchain remain relatively new and are still in the nascent stage. Banks should use the start-up approach to incubate and develop AI & blockchain solutions. Building a dedicated team to achieve rather uncertain objectives, however, may challenge the traditional bank culture.
- Regulatory Compliance: Even though some regulators have embraced open banking concepts, regulatory compliance challenges continue to plague the industry. Rules like SCA (Strong Customer Authentication) come at the cost of customer experience and compliance isn’t stress-free. AI solutions will have to undergo regulatory scrutiny to ensure compliance. Exposing and consuming APIs with third-party service providers will have to fulfill compliance rules and regulatory risk management expectations as well.
- Integration of Emerging Technologies: Banks will make quantum leaps in different new areas. However, integrating emerging technologies into their existing platforms will be an uphill task. For example, imagine a bank carrying out machine learning-based predictions to come up with recommended outcomes and automate its processes according to each outcome. This will involve AI algorithms running and feeding their output as input to RPA creating complex scenarios to handle and integrate.
All these advancements will yield results in terms of better customer satisfaction and improved revenue streams for banks.
The key to making the most of these developments lies in having appropriate strategies in place. Strategies that prioritize directions, proactively assess customer sentiments, and rethink business models are the need of the hour.
The article was originally published on Banking Exchange and is reposted here by permission.