Trends that are about to transform the banking and financial services have already emerged and are heavily influenced by technology and the need to minimize associated risks. From process automation and making decisions using big data to risk assessment, banks are increasingly adopting different solutions to improve customer experience and stay ahead of the competition.
An increasing number of financial institutions rely on robotic process automation to improve accuracy and ensure that operations are more efficient. Examples of applications include processing of credit card, mortgage, and loan applications, updating the general ledger, processing real-time inquiries, and cybercrime protection. Using robotic process automation offers many benefits such as minimizing processing errors, ensuring accurate loan verification, and managing large volumes of data. RBA also allows for accurate updates of data in the general ledger, including liabilities, assets, expenses, and account payables and receivables, which are included in financial statements.
Artificial intelligence will also lead to increased automation of processes and tasks, thus improving customer experience. AI has different applications such as using voice assistants and chatbots to interact with customers, monitoring for fraudulent activity, and use of smart contracts.
Making Decisions Using Big Data
Financial institutions generate huge volumes of data on a daily basis, which is mainly done through individual transactions. Customers use their smartphones to pay bills, deposit checks, monitor their balances, and more. When they use online banking, purchase products and services, do research, and comparison shop, users generate data. This can help banks to gain insight of market trends, portfolio performance, and customer experience. Using big data analytics can help financial institutions to analyze feedback, personalize their products and services, improve risk management strategies, and create customer profiles. Customer profiles, for example, offer important insights such as service and product preferences, banking behavior and patterns, products that clients use, number of accounts, and demographics.
Security Is Paramount
Blockchain is increasingly used by finance providers to ensure that transactions are safe and secure. All transactions are verified without the need for third-party authorization. Acting as a form of a distributed database, data is shared among multiple users, which eliminates the risk of misusing it and improves transparency. Blockchain helps prevent fraud by making use of digital signatures that are based on private and public encryption codes.
Banks face different types of security challenges such as botnets, ransomware, stolen devices, and malicious code. Other forms of attack include social engineering, phishing, malicious insiders, denial of service, web-based attacks, and malware. Blockchain helps prevent security attacks of different kinds by encrypting data which makes it impossible to manipulate it for the purpose of fraudulent transactions. Confirmed transactions cannot be altered in any way. Among the many benefits for finance providers are improved intra-bank communication, data integrity, encrypted metadata, and secure agreements. At the same time, blockchain solutions still have a more limited application because they are expensive to develop and complex. Experts also claim that blockchain systems can only have two out of three components but not all. These are security, scalability, and decentralization. Scalability, for example, refers to the fact that they have more limited application in some sectors than others. Decentralization is another component which refers to the extent to which value, influence, and ownership are diversified. Some experts note that decentralization does not work well for big PoS-based networks.
Mobile and Online Banking Services 24/7
We are travelling more than ever in an ever globalizing world, especially before the global pandemic hit. Bank customers cross time zones all the time, making 24/7 on-demand mobile banking important. More and more financial institutions feature apps to offer support round the clock. Chatbots are increasingly used to respond to customer inquiries outside banking hours when no one works. This not only helps reduce costs for providers but enhances customer experience. Apps allow clients to make transfers, use digital wallets, and make payments from anywhere in the world. They can manage their personal finances efficiently and securely, including transfers, loans, deposits, and accounts. Mobile apps are used for loan repayment and origination, with support available for loan, deposit, savings, and current accounts. Mobile banking also offers access to a large variety of transactions such as bulk payments, mobile wallet transfers, international payments, standing orders, transfers to other financial institutions, and intra-bank transfers. Apps also allow for secure authentication through integration of third-party services, software token for strong customer authentication, eSignature, OTP, PIN, and biometrics.
Mobile and online banking services offer added benefits for customers, including smart notifications and alerts. These can be in the form of email alerts, text messages, and push notifications.
There are benefits for banks too, including centralized administration and control, improved security and reliability, and efficient legacy systems. New mobile banking systems also allow financial institutions to integrate different channels and make use of emerging and cloud technologies. Finally, providers benefit from partner integration, centralized monitoring, and data integration.
Data allows finance providers to identify customers with low-risk and high-risk profiles. Machine learning solutions process information to monitor transactions and user location and behavior and thus prevent fraudulent transactions. Banks increasingly use machine learning and deep learning to process multiple streams of data and make decisions regarding security risks.
The global financial crisis placed an increased emphasis on risk detection, assessment, reporting, and reduction. Trends in detection and management are influenced by customer expectations, new policies and regulations, and new types of risks that are expected to emerge. According to experts, machine learning has the potential to create more precise models with better predictive capability. The addition of new streams of data over time helps improve their capacity.
Banks face multiple challenges, including insolvency, liquidity, sovereign, foreign exchange, operational, credit, market, and interest rate risk. Foreign exchange risk, for example, refers to changes in the value of liabilities or assets resulting from exchange rate fluctuations. When it comes to credit, there is always a possibility that borrowers fail to repay principal and interest amounts. Machine learning can help offset such risks by mining through huge volumes of data and extracting useful information. It comes from different sources such as metadata, customer interactions, consumer apps, etc. To boost their analytical capabilities for risk detection, banks are increasingly adopting AI and machine learning. There are different applications for banks to explore, including monitoring for conduct breaches, detection of money laundering and fraudulent activities, and risk modelling.
Credit risk monitoring is important for financial institutions and is based on loss given default and exposure at and probability of default. New algorithms have been shown to perform better compared to traditional formulas.
Banks also use a variety of methods for credit scoring, including nearest neighbor, Bayes classifier, logistic regression, and discriminant analysis. Again, artificial neural networks yield better results when it comes to classifying customers as creditworthy or non-creditworthy.
In general, banks use a number of tools and algorithms for risk mitigation and detection, including standard and machine-based solutions. These include random forest, extreme machine learning, fuzzy rule based system, survival analysis, and logistic regression.