If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
As banks and credit unions pivot from managing the impact of the pandemic to reopening and repositioning business models to reflect a more digital economy, it is clear many of the changes in consumer behavior will be altered forever. From the way consumers shop for new financial services to the way they transact and interact, we are beginning to understand that consumers are expecting digital experiences to be central to all stages of their customer journey. But digital capabilities and improved customer experiences don't operate in a vacuum. In a digitally empowered world, financial institutions must leverage the power of big data, AI and machine learning to drive customer engagement and conversion. To accomplish this, many institutions are moving to the cloud, hiring data scientists and officers, and finding marketers who understand how to bridge the gap between the pace at which data is generated and the ability to create real-time engagement.
Data Push: Push-based strategies are the default model. Automated the delivery on pre-determined specification, a forwarder is installed close to the source of the data, or built into the data generator/collector and pushes the events to an indexer. Data Pull: This approach provides significant flexibility by letting you create reports from multiple data sources and multiple data sets, and by letting you store and manage reports with an enterprise reporting server. Pull based cannot be reliable for real-time reports and information. Also, Pull base system most tolerate, its lack of real-time information cannot be best fit for supervisory Financial Institution as they demand real-time reporting with greater insights to financial health conditions of FIs. Supervisors can use machine learning tools to create a "risk score" for supervised entities. FINTRAC, the Financial Transactions and Reports Analysis Centre of Canada, has created one such score, evaluating the risk factors related to an institution's profile, compliance history, reporting behavior, and more.
For global banking, artificial intelligence (AI) could potentially deliver up to US$1 trillion of additional value each year, boosting revenues through increased personalization of services, lowering costs through efficiencies, and uncovering new and previously unrealized opportunities through the use of data, says McKinsey & Company. In a post titled AI-bank of the future: Can banks meet the AI challenge?, McKinsey says that banks have continuously adapted to the latest technology innovations throughout the years, and as the industry heads towards the AI-powered digital age, incumbents must adopt AI at scale and become so-called "AI-first banks." Several trends are accelerating banks' transition towards becoming AI-first, it says, with the first one being customers' rapid adoption of digital banking. COVID-19 has further boosted the adoption of digital banking with use of online and mobile banking channels surging an estimated 20 to 50% in the first few months of the pandemic. The emergence of digital ecosystems and so-called "super apps" is also changing the way consumers discover, evaluate and purchase banking products and services, it says.
Despite all the excitement around innovations in fintech, larger and more traditional financial institutions are typically a little bit slower to adopt due to their complexity and size. Particularly with artificial intelligence, traditional financial services have always been eager to capture its potential but sluggish to fully implement. However, this has changed rapidly due to the onset of COVID-19. In the past few months, there has been over a 200% increase in mobile banking registrations and an 85% increase in mobile banking activity. This is a trend that is unlikely to change soon.
Digital banking transformation involves the integration of data, advanced analytics and digital technology into all areas of a financial institution, changing the way work is done, priorities set and services delivered. More than just a technological upgrade, digital transformation requires a cultural change that challenges legacy processes, encourages innovation, and rethinks all aspects of risk and reward. The objective of an organization's digital transformation process might be to improve the customer experience, reduce costs, streamline operations, reduce friction, become more agile or increase profitability … or any combination of these objectives. In any case, digital banking transformation will disrupt business models that have been the foundation of the organization for decades. This is why true digital banking transformation is so difficult to achieve – it's more than simply delivering the same product on a new app.
In the given unprecedented times, digital transformation is vital. One of the significant challenges is modernizing banks and legacy business systems without disrupting the existing system. However, artificial intelligence (AI) and machine learning (ML) have played a pivotal role in conducting hassle-and risk-free digital transformation. An artificial intelligence and machine learning-led approach to system modernization will enable businesses to associate with other fintech services into embracing modern demands and regulations while ensuring safety and enabling security. In the banking industry, with the growing pressure in managing risk along with increasing governance and regulatory requirements, banks must improve their services towards more unique and better customer service.
Mitigate risk management – One of the best examples to showcase the benefits of Machine Learning can be described here. Back in the days, while providing loans to customers, banks had to rely on the client's history to understand the creditworthiness of that respective customer. The process was not always accurate, and banks had to face difficulties in approving the loans at times. But with the digital transformation, the machine learning algorithm analyses the customer in a better way to process the loan in a much convenient manner. Protecting fraudulent activities – Banks are already one of the most highly regulated institutions and must comply with strict government regulations in order to prevent defaulting or not catching financial crimes within their systems.
There is a fresh wave of disruption post COVID-19. Banks and financial institutions are adapting digital transformation at a blazing pace, this is a good and a progressive sign for us as this opens doors to the much awaited advancements in the financial sector. Lets just say – now the digital revolution has truly begun. The COVID-19 crisis has triggered customers to adopt digital interaction across segments. Nowadays branch loving customers are also using digital platforms to interact with their banks or NBFCs (Non-Banking Financial Companies) – this routine may become a trend in the future.
Machine Learning (ML) is reshaping the financial services like never before. It has become more prominent recently due to the availability of a vast range of data and more affordable computing power. It helps financial companies and banks to stand out of the box and achieve desired business growth. In the modern era, financial institutions are running a race towards digitisation. Staying ahead of technological advancements is a mandatory resort for them.
The attention on technology and risk in financial institutions has often been laser focused on transactions. That makes sense because of the large volume of trades and other transactions that are at risk from fraud, hacking and other issues. However, financial institutions also have other risks. Organizations have been slowly increasing the use of artificial intelligence (AI) in other areas, and there are signs that AI is supporting a convergence of risk management tools in financial institutions. Financial transactions, trading in particular, have long been a focus of technology.