Big Data Analytics/
Artificial Intelligence (BDA/AI) for Financial Institutions
AI Driven Scoring Analytics
AI Driven Collection Analytics
AI Driven Fraud Detection System
AI Driven Marketing Analytics
Disruption is certain in finance industry. Fintech disrupts Banks, period. Agile business process, advanced mobile technology combined with deep data-driven insights about customer will enable fintech start-ups to compete and soon conquer incumbent players such as banks and finance companies with old paradigm and technology.
We are witnessing the situation where smartphone transactions have successfully surpassed branch transactions and banking business will continue to move digital along with Big Data Analytics/Artificial Intelligence, Robo Advisory, Blockchain, and Wearable Devices. Data science and machine learning becomes the new core of the bank, and HR department is pushed hard to hire many data scientists. The ability to leverage off data, capturing more data points, and analyze data using machine learning algorithms will enable finance institutions to identify new opportunities, new segments, new behaviors, and also the new customer’s patterns. This complex data sets allow them to exploit their resources by develop new products, new strategy, and new business process in order to maximize profit and minimize cost.
The Synergy
The synergy between Big Data, Data Analytics and AI enables companies to improve and innovate, rethink their business models, and deliver fully customized service to their customers. Some use cases pertaining to the financial sector are listed below.
Intelligent Accounts
Financial institutions can offer their customers a new account concept with a value-added service that can analyze their behavior on the basis of generated expenses, provide expense and savings forecast information, categorize transactions to view them by group, or check product recommendations that meet their specific needs.
New Business Opportunities
In addition to their internal customer information, banks now can also access external information, such as data form social media or online behavior, to add to the data ecosystem surrounding each customer. This opens up new business opportunities for the banks: if their customer uploads photos of certain type of car and indicates their interest, the bank can offer a credit product immediately, meeting the customer’s needs at the right time.
Recommendation Engine for Branch/ATM Placement
Based on data about the areas their customers frequently visit, time of visits, where they shop, and other customer demographics, financial institutions can apply analytics to determine where a branch of ATM should be placed to generate the most benefits, and even the right amount of cash to add to each ATM.
Risk Management and Fraud Prevention
Traditionally, risk management and fraud prevention are two of the most important issues for banks. Big Data technologies enables banks to detect fraud in near real-time, allowing more effective preventive actions and mitigating risks.
Resource Optimization
Data about the institution’s operations, processes and resources can be collected and analyzed, enabling new patterns and behaviors to be discovered. These insights can be translated into changes in operations, process re-engineering and resource allocation, allowing the institution to maximize benefits while incurring fewer expenses.
Credit and Collection Management
Vast amount of customer data enables financial institutions to predict, analyze, and manage customer’s credit worthiness more accurate and efficient. Big Data implementation will provide 360 degree of customer information which helps risk manager to make better decision.
Churn Prediction
It is always more economical to keep an existing customer than to attract a new one. Most banks analyze a customer’s account activity and combine this information with internal data from other channels (branch or online) to determine whether the customer is about to leave the bank. Big Data allows data from external sources (such as social media) to be included, enriching the bank’s customer data, improving the speed and accuracy of churn prediction and potentially providing more channels through which the bank can engage the customer to entice him or her to stay.
Best Means of Customer Communication
Customers are likely to react better when contacted through their preferred or default channels, e.g. social media, email or instant messaging. The financial institutions must analyze and determine the channel that the customer feels the most comfortable with and send them notifications through this means. Traditional practices of sending notifications to all customers through the same channel (that not all customers use) generate poor results and cause unnecessary expenses.
Products
Skyworx has capabilities to enable banks and financial institutions overcoming the challenges in new technologies. With more than fifteen years of experiences in Indonesia’s banking and finance industry, Skyworx has established vast knowledge related to Big Data Analytics and Artificial Intelligence (BDA/AI) implementation for financial institutions. Skyworx has been consulting many prominent players in this industry to exploit the benefit of BDAI/AI. Our solutions are designed to equip companies with the edge capabilities to significantly improve the productivity and reach new higher standard.
AI-Driven Marketing Analytics
The integration of meaningful data to perform marketing analytics like customer segmentation can help organizations understand their customers at a deeper level, and come up with strategies to win-back inactive customers and convert them to active customers, as well as to identify opportunities for cross/up-selling.
AI-Driven Scoring Analytics
Financial institutions judge consumer creditworthiness on frequent basis. Errors and inaccuracies in this process cause an increased value of outstanding loans which will not be recuperated by banks due to default.
Failing to comply with payment obligation can mark consumers for years, lowering their consumer creditworthiness and making it even more difficult for them to obtain a future loan. These are concerns of both authorities and banks when designing a financial product and its application process. In order to solve these problems accompanied by structural consumer debt, Skyworx developed AI-Driven Credit Scoring Analytics. Conventional credit scoring methods at traditional financial institutions are becoming less relevant in today’s age of massive data generation. Millennials are well-connected and more digitized than ever before. This leads to new possibilities when looking at the contents of new data and the applications that are possible with thorough analysis of great representative quantities.
AI-Driven Collection Analytics
The Collection Analytics is able to optimize performance data and leverage it using a diverse, multi-channel communication approach.
Phone calls may be included as part of a larger strategy, but these platforms are primarily built around modern consumer channels including email, SMS, social media, push notifications, and direct drop voicemails. Each of these implementations of machine learning help to build a more personalized, more focused, and more forward-thinking debt collecting experience for both consumers and creditors. The algorithms will process large sets of data such as call times, call effectiveness, the value of certain accounts, collections rates, and many other variables. By analyzing this information, teams can optimize their outreach strategies by focusing on accounts that are more likely to be collected on, understand what times of day or channels work the best, and even determine what language to use in conversation with specific subsets of accounts.
AI-Driven Fraud Detection System
The application of machine learning in anomaly detection is popular and has been widely used in the industry mainly for 3 reasons: speed, scalability and efficiency.
Firstly, machine learning is less time-consuming in which it does not involves a lot of manual interaction, and can allow users to evaluate a huge amount of transactions. Especially in this era with the vast amount of data that can be utilized, the evaluation time will be lessened with the help of machine learning. In addition, with more and more meaningful data being fed in, the algorithms and models become more effective by achieving a higher accuracy. Another one good use is that in situations where there are hidden and subtle events in the user behavior that may not be obvious, machine learning is able to uncover these hidden correlations between the user behavior and the likelihood of fraudulent actions. This can lead to new discoveries of fraud methods and solutions. Any findings can then be followed up with the appropriate actions and the utilization of a dashboard will be meaningful to quickly display some of the meaningful results for critical decision-making by the management, as well as for daily operational usages (I.e. Drill-down, alerts, automations etc.) by the analysts.
AI-Driven Marketing Analytics
The integration of meaningful data to perform marketing analytics like customer segmentation can help organizations understand their customers at a deeper level, and come up with strategies to win-back inactive customers and convert them to active customers, as well as to identify opportunities for cross/up-selling.
We developed more effective methods to perform marketing analytics and derive actionable insights. This will allow client’s marketing team to better understand their customer and business performance for their operational usages, with the aim of reducing customer churns and increasing sales.
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