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Redefining Customer Experience with Open Banking

Redefining Customer Experience with Open Banking

In the past decade, digital transformation has been an integral part of the banking revolution. Digital drives the need for banks to better engage with their customers and improve overall customer experience. In today’s fast-moving millennial world, a shift in user power demands better engagement and real-time interactions. In this environment, open banking brings in new possibilities of creating a customer-centric ecosystem.

With the introduction of the Revised Payment Services Directive (PSD2) in Europe and similar legislation around the world, open banking has gained significant ground in the past year. Open banking driven by mapping and understanding a customer’s journey to enable better interaction and experience. The bridge being – application programming interfaces (APIs).

APIs lies at the core of open banking, emerging as the main mechanism for data interactions between banks and third-party providers (TPPs). On one hand, they have been able to provide the much-needed integration between various systems and on the other, they have been facilitating data integration across surround systems. Organizational structure and culture are shifting to support rapid product development and innovation in the financial sector.

Globally, banks are recognizing the potential of Open Banking in enhancing their service offerings, customer engagement, and increase revenue potential from new channels. But open banking demands new approaches to drive customer engagement. To meet the increasing demands of the market, banks have to build on

  • Better anticipation of the customer through better insights
  • APIs enabling engagement through customer interaction
  • Driving impact through ongoing innovation

Better anticipation of the customer through better insights 

APIs enable banks and financial institutions to push past the current banking system and develop and deploy products based on what customers will need. Banks are tapping into social media and other digital channel data to determine customer needs and behavior. With the right insights, banks can develop a deeper understanding of what customers anticipate and create micro-segments to improve marketing, operations, and business focus. For example, if a customer’s digital footprint indicates an impending property purchase, banks can make a timely offer of attractive housing loans.

Many banks are introducing AI-powered chatbots, backed by conversational AI abilities, to improve engagement. Financial advisory bots, such as Eno from Capital One or Ally Assist from Ally Bank, offer financial management solutions and easy banking facilities. Chatbots utilize APIs to integrate with data management platforms; allowing banks to analyze the extracted data and derive insights to anticipate customer behavior.

APIs enabling engagement through customer interaction 

APIs are helping banks achieve better transparency and visibility, drive innovation, and improve collaboration. For instance, APIs are enabling the creation of vast customer data repositories to help deliver a highly personalized experience to consumers. Banking platforms provide TPPs access to multiple data sources, including banks direct deposit accounts, credit cards, investments, and other financial data for developing innovative financial applications and services. With quick integration and seamless data access, financial APIs can be used to construct a detailed customer profile and personas to increase convenience and connecting them with the right products or services. In its recent whitepaper, FICO categorizes the new-age consumers into five categories – success-driven savers, precarious passives, ambitious adopters, delayed dreamers, and fiscal futurists. These categories are based on user behavior and attitude toward financial institutes, products, and services.

Canada’s digital-native bank, Tangerine, partnered with IBM to develop a mobile banking app and provide a ‘shake to feedback’ feature. This capability offers customers an easy and accessible medium to provide personalized feedback directly to the bank; effectively engaging with the customer and gaining insights to improve the overall mobile experience.

Driving impact through ongoing innovation 

For long, banks have been held back in the technology race due to their monolithic legacy systems and data silos. Migrating to new systems is an expensive affair that most small to medium banks cannot afford. A second challenge is staying relevant in the presence of technology giants Google, Apple, Facebook, and Amazon (GAFA).​Deloitte estimates that 75 percent of millennials would be more interested in new financial services from GAFA than banks.

To survive in the current digital atmosphere, banks have to modernize their legacy systems to become more agile, flexible, and support scaling up plans. With an API strategy, it has become possible to adopt a bi-modal IT to improve speed and efficiency. Using APIs, banks can repackage core system assets to create new and innovative systems of engagement. Banks are now able to connect legacy systems for better operations and simultaneously improving their front-end for better customer experience.

Considered an enabler of innovation, APIs are expanding banking ecosystems to include more financial services and products that emphasize consumer value propositions. For instance, a Germany-based FinTech and fully licensed digital bank, is helping companies become financial service providers. Using its regulatory and technology infrastructure, the firm has developed a modular banking kit that includes APIs for account and transaction services, compliance and other services.

The future of banking would be a new paradigm driven by emerging technologies, non-traditional competitors, and deregulation of the sector pushing for openness and transparency. In order to serve customers efficiently, banks have to emerge to manage relationships among multiple stakeholders making up the banking ecosystem. With APIs, banks can truly become a digital platform and a central hub for TPPs to interact and attract more customers.


3 modes in which digital transformation can be done

3 modes in which digital transformation can be done

Today, as digitization sweeps through the banking industry, CXOs are working on digital strategies to accelerate business performance and operations. While customer-centricity has been the mantra of the banking industry, evolving technology and customer needs pose a problem establishing banking services and products.

With digital banking and omni-channel experiences becoming common expectations, banks have to effectively strategize their digital resources. But tackling the digital landscape can be daunting. A recent survey by BCG stated that only 43% of organizations have a “clear digital strategy for the corporate bank as well as a well-defined roadmap for digitization”.

To decide the best digital transformation strategy for your organization, PwC recommends three approaches:

  • Front end only – CX improvements with no changes to the IT infrastructure
  • Wrap and digitize – Digitization of individual components of banking functions
  • Go digital native – Digital customer interface with a complete digital back end
Front end  Front-end only

Today’s tech-savvy customers are used to seamless online transactions and one-click applications. Fintech see a growing customer base with their offering of a wide range of services with an easy to navigate interface. For banks to compete in this environment, the first simple approach would be to digitizing the customer interaction channels. These include building a website or a mobile app to cover basic aspects of customer interactions with the bank.

The focus of this approach is on only improving the  CX and customer-facing systems. Viewed as a ‘cosmetic-fix’, helps banks put up a digital front without having to invest in changing the legacy systems. It is one of the quickest approaches and would give the banks the initial push towards digital transformation.

The cosmetic digital front has helped banks stay afloat in a competitive environment. But for true success, digitization of front end should be swiftly followed by integration of back-end systems to significantly improve customer service. Back-end operations cannot hide behind the digital front for too long without increasing operational costs. In order to scale up, back-end systems have to be updated.

For example, Digibank introduced the first mobile-only bank in India. The application implemented biometrics and artificial intelligence (AI) for a seamless paperless, signature-less and branchless bank. The easy onboarding process of less than 90 seconds has helped increase their customer base to over one million in just one year. The success of Digibank is a mix of right technology and marketing. Currently, Digibank offers limited products and services such as money transfers, bill payments, and ATM services. But for the organization to become a standalone digital bank would depend on its back-end process. As the bank looks to further expand its digital transformation efforts, it is working on alternate digital strategies to solve its sustainability issues.

Digital  Wrap and Digitize

A longer process to digitization would be the Wrap and Digitize approach in which, the front-end digitization is supplemented with replacing/upgrading legacy infrastructure with digital technology. This approach is effective as it integrates the middle and back offices during the setup process.

Banks are adopting the ‘Wrap and Digitize’ approach as it significantly improves customer experience compared to the front-end only approach. The process focusses on improving individual components of banking functions. As per PwC, banks can use APIs to integrate their data, functions, services, and products under one roof. The flexibility provided by the API enables the organization to be more agile in its operation. For example, the next phase of digital transformation for Digibank involved improvements in their back-end systems. A new code was built over the existing back-end assets to support rapid growth and expansion strategies. The new architecture enabled API-based banking and a digital platform for banking through multiple business partnerships.

The lengthy process of wrap and digitize can be a deterrent for banks. Every process is addressed one-by-one before moving onto the next one. The transformation process can take several years to be completed. Despite the long duration, the process is the most cost-effective as the investments are spread out over a period of time. The gradual approach of integration works as the best option for traditional banks and credit unions.

Digital native  Go Digital Native

PwC’s digital native approach is for challenger banks and digital-only banks poised for rapid growth. Banks start by creating minimally viable banks (MVBs) that offer limited services or products. By being digitally native, banks are built on a digital core and open architecture enabling the development of a fully-functional agile organization. This approach emphasizes on customer-centricity and enables banks to shift their operations based on customer preferences.

The functioning of a digital bank would require an infusion of digital mindset into the traditional banking atmosphere. Digital banks also face significant regulatory issues that have been written for traditional banking sectors. For example, in the United States, digital banks are expected to meet the same regulatory standards, reporting and consumer protection regulations as incumbents. But agile organizations like DBS, are using third-party services that leverage AI natural language processing (NLP) to manage regulatory compliance across governing bodies.

Digital disruption in banking is pushing banks to rethink its business models. To follow the path of digital transformation banks would have to determine their long-term strategy before choosing the technology. While there are several ways to decide the path to digital transformation, we outline a basic guideline for organizations:

  • Multidisciplinary thinking – To ensure the viability of an organization, develop a long-term strategic plan taking different perspectives of business, IT, compliance, and operations into consideration. The multi-faceted approach enables the development of an agile organization that is able to support quicker product development. Adopting a digital-first strategy enables growth that is not limited to existing technology and channels.
  • Functionality list – Deciding on the best digital banking platform is easier when the functionality of each platform is listed and understood. The solution selected should align with your long-term strategy, requirements, and goals.
  • Vendor cultural fit – Analyse your strategic partnerships and vendors for cultural fit with your organization. Vendors that leverage innovative technology are preferred as they enable an open-architecture approach. Connecting the ecosystem to a third-party service future-proofs your bank from modern technology disruptions.
  • Involve internal stakeholders – Get your teams to work closely with internal stakeholders, like senior management, to develop minimum viable products in the implementation phase itself. This approach ensures the reduction of time and cost while gaining valuable insights through customer testing.

Digitization is radically changing traditional banks and credit unions. Many banks have recognized the importance of differentiated digital strategies and working toward complete digital transformation.




Making a Difference through Automation in AML

Making a Difference through Automation in AML

The rise in money laundering and terrorist financing cases has led to a global awareness on financial loss and its impact on the economy. Over the years, regulators have increased pressure to monitor every financial transaction for criminal activity, terrorism, and tax evasion. The penalties for non-compliance are staggering – a recent example being Deutsche Bank which was fined $41 million in 2017 for money laundering lapses.

Banks and financial institutions (FIs) have been exploring multiple software solutions to reduce their operating costs on AML, at the same time maintaining an efficient system that delivers accurate compliance reports in time to stakeholders and decisions makers. Financial ecosystem players – FinTech and RegTech, have been continually evolving to develop robust solutions, through inclusion of AI-based initiatives and automation.

A Thomson Reuters survey indicates that an average financial institution spends close to $150 million on AML/KYC initiatives. The prominence of data collection, aggregation, and analysis combined with a high degree of repetitiveness and process-oriented approach, makes AML an ideal candidate for achieving cost benefits and efficiency through automation.

Automation can be achieved across multiple areas of AML, prominent ones being Know Your Customer (KYC), Customer Due Diligence (CDD), anomalous transaction monitoring and even Extended Due Diligence initiatives. Based on the maturity of the process under consideration, it can be completely automated or partially automated by bringing in the human intervention at suitable intervals. Few of these are listed below.

Customer onboarding

While many of the data collection processes at this stage like gleaning customer data from accessible sources like the bank’s CRM system can be automated, a lot of time is spent in carrying out KYC. Majority of the current KYC processes may take days/weeks to comply with regulators. A global survey done by Thomson Reuters on KYC indicates that the time to onboard has jumped from 28 days in 2016 to 32 days in 2017 and would continue rise by at least 12 percent in 2018. Further, financial institutions with revenues in excess of $10 billion have witnessed an increase in KYC spend from $142 million in 2016 to $150 million currently.

Many banks have started exploring Artificial Intelligence/Machine Learning based automation systems across their KYC initiatives. Processes around validating customer data by scouting across various surround systems, compliance directories, social media feeds and regulatory bodies can be fully automated. Similarly, screening of the customer information through OFAC and PEP checks as well as external government watch lists can benefit through automation. The latter can be extended not only to new customers but also to existing customers of the bank.  Multiple solutions in the market take automation a step further by validating the identity of applicants in real time, thereby drastically reducing on boarding times.

By using RPA a European bank was able to automate customer checks and make information readily available to analysts for clearing CDD compliance. The time taken drastically reduced by 80% from 20 minutes taken initially.

Customer risk profiling

Building customer risk profiles is an important step towards identifying suspicious activities and flagging off investigations. It involves data collation not only through established sources like legal registries and directories but also the broader online space including social media networks, websites etc. While data is collected at the time of onboarding, there needs to be mechanisms in place to constantly scout and make updates on an ongoing basis.

Currently, financial institutions use manual intervention in the processing of structured and unstructured data. The high cost of integrating systems is a major deterrent to automating this process. This makes it an ideal platform for using BOTS that can crawl around frequently, update information to customer information as part of risk profiles and provide quick response to any customer risk assessment requests.

Further, technologies like Artificial Intelligence and Blockchain are increasingly being used to carry out real-time reporting that compiles, tracks and stores large data sets while adhering to regulatory rules set by different financial agencies. Few leading banks are also experimenting using chatbots for quick and easy KYC compliance of customers. The bots analyze user responses using Natural Language Processing (NLP), in turn reducing time and labor requirements for KYC processes.

Suspicious activity monitoring

Research reports on the global AML software market indicate that transaction monitoring has the highest penetration rate. Little wonder that regulatory solution providers like Nice Actimize and Pega systems have been incorporating RPA, machine learning, and analytics principles to automate suspicious activity monitoring solutions. On one hand, such solutions provide better accuracy in reporting anomalies and on the other, free up financial crime experts to look at higher category threats.

On similar lines, various RegTech players have been attempting to enhance automation across this space through Anti-Financial Crime Solutions that aim to understand customer behavior, identify patterns coupled with unstructured text analysis, and detect even the smallest anomalies in transactions. Such initiatives further help in reducing the number of false positives by having an accurate risk profiling of the customer that can be mapped to flag off truly suspicious transactions.

We also see that newer and effective systems are continually replacing their predecessors. Today’s BSA/AML programs are becoming increasingly reliant on quantitative models (like Bayesian networks) to detect suspicious activity. Bayesian frameworks are being used to assist in building a risk score for customers, essentially identifying customers who need to undergo the EDD process. These are further used to build customer profiles and drive automated Suspicious Activity Report (SAR) filings, based on anomalies detected in their transaction histories.

While automation seems to be the magical process to solve all AML issues, banks need to recognize the limits of AML automation. Technology decisions need to be customized to a bank and not blindly replicate other systems. Financial institutions also need to note that not all processes can be automated. Some processes like transaction investigation can be semi-automated wherein specifics associated with an anomalous transaction can be provided by automation system while aspects relating to analysis, classification of the transaction and rectification measures are carried out through human intervention.