The Ultimate Guide to Digital Transformation in the Lending Industry
Digital transformation is a demand for all forward-looking businesses operating digitally and wishing to stay relevant and competitive in the coming years. So I’ve prepared a detailed guide on the nuances of digital transformation in the digital lending industry.
The guide consists of seven parts, each dedicated to a specific aspect of the transformation process:
- Step-by-step guide for including digital transformation into your business agenda.
- The customer experience at the heart of digital transformation.
- Embracing the gray zone customers with digital transformation.
- The complete guide to data management in lending institutions.
- How Artificial Intelligence can boost your lending business.
- Software platform lenders need for digital transformation.
- Crucial things to consider when updating your decision-making solution.
The guide covers 15+ years of RDNpoint’s cooperation with lending organizations. You’re sure to find here exclusive tips for making your digital transformation efforts effective.
Part 1. Step-by-Step Guide for Including Digital Transformation into Your Business Agenda
The tech progress is in full swing, so the boundaries between various financial organizations are gradually blurring. The IBM Institute of Business Value experts found that 60% of modern lending business actors recognize this trend and expect to witness more competition from related industry representatives, such as FinTech startups and ecosystems unifying financial and non-financial activities. So, it’s not surprising that investment in digital transformation initiatives is expected to reach $2.4 trillion by 2024.
Statistics prove that a massive digital evolution is unavoidable, but will it go smoothly for all businesses? What barriers are lending organizations expected to face? And how can they overcome the challenges to succeed?
This section introduces you to the concept of digital transformation, lays out its business implications, examines alternative paths for transforming a digital banking business, and looks deeper into the most popular causes of transformation failures. We also give a bonus tip on proper staffing for a smooth digital transformation.
The origins of digital transformation in the lending business
Digital transformation in the lending sector goes far beyond software upgrades. It’s more about a deeper cultural mindset change incorporating new approaches to management, communications, and operational processes.
The rise of software technologies brought about digitalization to all spheres of human activity, not leaving the economy untouched. In fact, the economic sector was at the grassroots of adopting digital innovations and encouraging the advent of FinTech in 2008.
The global economic crisis also played a role in the transition, as it challenged the financial actors to reduce expenditures and improve service quality for survival.
That process coincided with the massive adoption of mobile technology and the growing popularity of the Internet of Things (IoT) and blockchain. All three, coupled with other tech trends, enhanced each other to cause a strong stimulus for digital transformation in the lending sector.
What does “digital transformation” mean?
The customer lies at the heart of digital transformation. Forward-looking businesses reconsider the customer’s place in their processes and adopt a customer-centric mindset to improve the customer’s experience. As a result, they reap better retention rates, higher satisfaction, and sustainable operations.
Talking specifically about lending organizations, we mean an in-depth operational change, expansion of service range, and setup of a partner ecosystem to ensure a seamless user experience across digital channels.
The bitter truth about digital transformation
Unfortunately, the real path of businesses toward digital transformation is far from ideal. As McKinsey & Company experts revealed, over 70% of initiatives fail their transformation efforts. So, is the play worth the candles?
The major blunder of failing companies is an approach to digital transformation as an outcome rather than a process.
As a rule, businesses encounter the following problems:
- Irregular planning. Technologies come and go at a glimpse of an eye. So, planning a change and sticking to that plan for a year or so without review is like chasing the past. Your transformation efforts should be timely and relevant to succeed.
- Inward focus. A winning approach to transformation is a synergy of business efficiency and customer experience. Too much concentration on the internal business processes leaves the customer out of sight.
- Biased decision-making. Any company has a variety of stakeholder groups with competing interests. Instead of trying to please everyone, companies should make their transformation efforts as pragmatic and structured as possible.
- Wrong budgeting. Investing in digital transformation always pays off, but you should be realistic. The best way to go is to establish an expected ROI and structure your modernization efforts around those goals.
Three areas to be covered by digital transformation
Embarking on digital transformation means a change to your business’s customer service, organizational structure, privacy approaches, logistics, and sales, among others. We’ve singled out three aspects that the transformation should cover.
#1 Customer relationships
Digital technologies shed light on what happens during your clients’ contact with your business at any point in time. Innovative tools give insightful analytics that fuels more personalized marketing and sales efforts.
Thus, better customer experience is the most evident outcome of digital transformation that lets you keep a finger on the pulse of user demand, adjusting prices and personalizing offers on the go.
#2 Organizational processes
Digital transformation in the company’s infrastructure is sure to cause greater process changes, such as workflow digitalization and more efficient, automated operations.
Paperwork becomes obsolete in the modern digital workplace, and new tech allows effective networking and knowledge exchange in distributed teams.
These improvements inevitably bring the company to a new level of productivity and enhance its competitive advantage in the market.
#3 Management and governance
Improper management is to blame for digital transformation failures in most cases, as BCG experts found out. Even top performers may be afraid of and resistant to large-scale change.
Thus, successful transformation is an outcome of the strong political will of the company’s leadership. It’s vital to set clear goals and motivate staff for change, train the employees, and assign change agents and champions to the teams.
Two ways of transformation
You can follow one of the two transformation paths: radical (from scratch) or gradual (incremental). Here’s how both work.
A radical model presupposes creating a new business model and leaving the old ways behind.
- Quick results
- Ability to attract new, tech-savvy, ambitious staff
- Suitable for companies ready for substantial investments
- High risks
- Unpredictable results
- High cost
This model presupposes incremental changes in the existing processes.
- Management optimization
- Ability to test the new approach before the full-scale transition
- Gradual increase in required investment
- Low speed
How digital transformation impacts business success
A digital-savvy lending business is sure to enjoy greater customer loyalty. Besides this positive effect, digital transformation can produce a broader positive impact on the business.
Here are the issues it can resolve.
- Fragmented decision-making. After the transformation, you will make real-time decisions based on detailed analytical data.
- Inefficient customer relationships. Digital tools help set up a centralized, automatic CRM to stay in touch with customers 24/7 and deliver exceptional service.
- Mediocre service. Advanced analytics and communication tools allow greater service personalization, improving client interaction quality with your organization.
- Outdated communication. You can reach out to customers via different digital channels to create an omnichannel interaction experience.
- Delayed reporting. High-tech marketing and sales metrics monitoring tools can bring your reporting to a new level.
- Ambiguous goals. Innovative planning and communication tools can optimize your interdepartmental communication and enhance collaboration.
5 golden tips for those who are behind
It’s okay to feel that you’re lagging behind at some point in the digital transformation process. Here are some workable tips for optimizing your transformation efforts and achieving success.
Get started NOW. Moving forward in digital transformation is impossible if you don’t do anything and just watch your competitors succeed. You should take real steps toward the goal daily and stay committed.
- Analyze the market. It’s always a good idea to learn from others. Look around, watch your competitors’ digital transformation approaches, and add something unique to design your own strategy.
- Keep in mind that it’s not one huge leap. Remember that digital transformation can’t occur overnight. It’s a long process without a set deadline, so you should orient your team for a continuous, gradual change.
- Automate and hypothesize. Treat your digital transformation journey as a time of experimentation. Try as many tools as possible to find the best match for your business architecture and goals. It’s time to abandon the old ways of doing things and shift to better, more efficient processes.
- Be strategic. Adopting new technology is expensive, so you can’t afford to pay for everything at once.
Top 7 causes of digital transformation failures
Statista’s experts estimated the investment in digital transformation at $1.18 trillion in 2019 and $1.31 trillion in 2020. Sadly, most of that money went down the drain, as only 30% of companies attained their transformation targets. The ROI of the remaining 70% leaves much to be desired.
Fig. 2. Successful vs unsuccessful digital transformation outcomes.
Why do these failures occur, and how to avoid them? How can the integration of loan management software help a regular lending business succeed in the digitalization journey?
#1 No transformative strategy
A digital transformation is no simple software upgrade. It’s a broad shift in the cultural mindset, a change that brings about new processes and makes others obsolete. Thus, it’s unattainable without a concise strategy to guide the company’s efforts. To develop such a strategy, you need to:
- Find a profitable digital direction in the long run;
- Formulate realistic goals and milestones;
- Analyze the existing and missing resources to plan their acquisition;
- Assess the company’s readiness for change at the technology, processes, and staffing levels.
- Otherwise, your efforts will be fragmented and will fail to attain any long-term business benefits.
#2 No political will for change
Reluctance to change at the business management level is a frequent reason for failure. It’s typical for financial companies to be rigid in terms of business model and organizational structure changes, so introducing a big change may fail at the onset.
As Andrew Thorburn, ex-CEO of National Australia Bank, shared, a clear vision at the top of an organization’s leadership is the clue to success. Thus, it’s vital to ensure acceptance of the digital transformation idea at the highest level first.
#3 No necessary software
A unified platform for centralized management of business processes, technologies, and staff is a great starting point for effective digital transformation. It helps bring all innovation efforts together and track them in real-time in one place, such as a loan management software product.
#4 Skill gaps
Innovation is about technology, but you shouldn’t forget that it’s people who use that technology and apply it to the benefit of the lending business. It’s impossible to succeed in digital transformation if your staff doesn’t know how to operate cutting-edge tech and has no idea about the right business processes.
Poor staffing and low digital literacy of lending organizations’ staff were named as the reason for 80%+ firms‘ limited innovation potential. It’s imperative to onboard the right talent and provide in-house training for existing employees to equip them with skills and knowledge to help them embrace digital transformation.
In the chart below, we present the data from the 22nd Annual Global CEO Survey by PwC. Respondents were asked a question: What impact is “availability of key skills” having on your organization’s growth prospects?
64% of CEOs said that talented staff shortages hold back their innovation. Only 4% of respondents felt that the shortage of talent had no impact on their organization’s growth.
#5 Excessive reliance on IT
A lack of IT competency in lending firms often creates an excessive dependency on the IT department, which causes innovation efforts to stagnate. A workable solution to this problem would be tech knowledge dissemination and broader staff involvement in the practical transformation steps.
#6 Communication hurdles
Even giants with large budgets can fail in their digital transformation efforts if they fail to communicate the strategy and roadmap to their staff. That’s what happened to GE, Ford, and Procter & Gamble, according to the CNBC report. The companies embarked on the digital transformation path but couldn’t set clear, actionable goals for their employees, which caused a communication breakdown.
#7 Outdated technology
You can’t achieve full-scale transformation by using old tech tools. A transition to advanced technology lies at the heart of digital transformation, as it helps change your processes and overcome significant development bottlenecks. So, Forbes experts recommend dropping the outdated technology and focusing on consistent innovation rather than a one-time purchase of a new software product.
Employees you need for digital transformation
Most successful cases of digital transformation teach one vital lesson: it can’t happen without the right staff on board. A company can speed up the change and ensure its smooth flow by hiring a responsible expert to guide the process.
The Chief Digital Officer, Innovation Officer, Digital Innovation Officer, or Chief Innovation Officer may play this role.
No matter how you name that person, they should be tasked with integrating digital innovation into your business processes for maximum results.
Lending organizations should hire a change agent with a solid financial background, preferably in the lending sector, to unify the technological infrastructure with strategic goals of this business type.
Sometimes it’s a good idea to hire a dedicated transformation team to complete this task. Fresh minds from outside can bring in new perspectives and take an unbiased, practical approach to change planning. This way, you can quickly get effective results without distracting your in-house resources from their core tasks.
Make digital transformation a part of tour business strategy
Digital transformation is much more than going digital. It presses businesses to adopt new ways of doing things and transform their IT infrastructures to meet present-day market standards.
Buying new software or hiring a change agent is not enough, as too many companies fail in their transformation efforts. The absence of firm links between transformation and business strategy, slow decision-making, skill gaps, and political reactivity in the change efforts can bring your digital transformation to naught.
Onboard the right talent for implementing that change and enjoy the gains digital transformation will bring to your major asset – the customers. We’ll stop on it in more detail in the following section.
Part 2. The customer experience at the heart of the digital transformation
The customer experience (CX) is gradually entering the business development agenda of lending organizations. While only 14% of businesses investing in customer experience (CX) in 2019, the figure rose to 27% in 2020.
This change signals a broader mindset shift among companies that realize low rates can’t be the only factor for custom acquisition and retention.
The connection between digital transformation and CX
Customer retention is a number one priority in lending organizations. It’s much cheaper to keep a client than to acquire a new one, and digital transformation can become an effective tool for boosting customer retention rates.
As McKinsey experts recently found out, positive CX can increase customer satisfaction by 30% and raise the lender’s revenue by up to 50%.
So, how can lending businesses embrace the transformation to achieve a seamless, rewarding CX and satisfy their clients?
Here are some workable insights into the CX aspect of digital transformation.
All transformation efforts begin and end at the customer
All brilliant minds in the global lending business (e.g., Deloitte, Accenture, Gartner, and McKinsey) caution companies against ignoring the customer’s role in their operations. The truth is that a well-designed customer retention strategy brings a ton of business benefits:
- Lower costs of new customer acquisition (30% less);
- 1.5x less time on operations;
- 80%+ increase in revenues.
Businesses with sound customer retention efforts have achieved these tangible improvements in only 18-24 months. However, it’s vital to remember that while loyal customers’ retention stays high, at around 60%, new customers are harder to retain (only 5-20%).
Success story: DBS Banks’ customer retention strategy
One of the first banks to digitize customer retention strategies was DBS. The financial institution positioned itself as a technology company offering innovative banking products, which helped it retain a top leading position in the global banking sector for the past 3 years.
The major benefits of digitalization, according to DBS executives, are the transformation of customer experience into a seamless, joyful process and the optimization of DBS business operations. The bank’s staffing needs had been reduced by 50%, which freed the resources and funds for business expansion.
This case suggests that the #1 priority for any lending organization is to transform its customer retention approach using the benefits of modern technology.
How lenders can transform the customer journey
For your digital transformation efforts to produce tangible CX effects, it has to cover three major areas.
#1 First point of contact
It would help if you produced a positive first impression on the client for them to stay with your lending business. How can it be attained if lending companies require many documents and forms from clients at first contact in compliance with the KYC/AML regulations?
There’s still some room for improvement with digital tools:
- Data collection in a digital format.
- Automated documentation and scan checks in the completed applications for data recognition.
- Smart loan terms’ selection based on the proposal matrix and the 360-degree view of the client’s profile.
An ability to communicate promptly and competently is a critical CX factor. Since COVID-19, online and mobile banking use has increased dramatically, with only a 20-50% increase in the first months of the pandemic.
The mobile banking trend is expected to continue, with 70% of customers preferring multichannel interactions with their financial providers.
A lending organization can’t succeed without a user-friendly mobile app, even if it offers the best rates for lenders. As a result, modern businesses focus on mobile-first solutions in their digital transformation, directly contributing to CX quality.
Outstanding personalization is the new normal. The Capco survey revealed that 72% of consumers prioritize personalized service when choosing a financial services provider. With these stats in mind, lending organizations should reconsider their points of contact with clients and make them more personalized. Here’s how it can be achieved:
- Make personalized offers based on big data analysis and a 360-degree view of the client’s profile and history.
- Cross-selling and up-selling of the company’s services using omnichannel communication.
- Adjusting the prices to clients’ purchasing power and preferences.
- Using AI to make more accurate predictions and analyze non-standard user behavior.
- Integrating third-party data to inform more accurate loan portfolio design.
It may also work out to blend proactive and reactive personalization. Proactive steps involve generating new offers based on data insights, and reactive steps are flexible and timely responses to the clients’ signals. In both cases, customers feel valued and grow more loyal to a responsive, flexible lending provider.
Put the customer at the center of your transformation plan
When planning your digital transformation, don’t forget that customers are your most precious strategic asset. Customer retention is a key to sustainable business performance, and this goal can be best achieved by providing user-friendly, customized services through innovative digital channels.
Part 3. Embracing the Gray Zone Customers with Digital Transformation
Working with reliable clients with a clean credit history is easy and non-demanding. What can go wrong when you’re choosing a tried and tested population and issuing loans with minimal risks? Things change when you need to include a higher-risk category of clients – those in the so-called “gray area” – into your service portfolio.
How can lending businesses embrace this customer category safely with the help of digital transformation? Here, we examine the nuances of serving “gray zone” clients with an optimal risk/benefit balance.
How lenders can expand their client portfolio
Over 1.7 billion adults are unbanked globally today. It means that these people have no access to the basic lending services that most of us take for granted. People who belong to the gray zone are usually:
- Graduates planning to start a business.
- Self-employed entrepreneurs who need a loan to expand their business.
- Women with small children.
- Career changers.
- Customers with a blank credit history.
Lending organizations typically treat these populations as risky, but together with a certain amount of risk, they also conceal a huge business potential.
Tips for using digital transformation with “gray zone” customers
If you develop a more advanced risk management system, you can serve the “gray zone” clients with lower risks for your business. This outcome may be achieved in three ways.
#1 Data-based decisions
Big Data can say much about credit risk, informing wiser and more flexible credit decisions. You can take advantage of rich datasets available in the digital space to build your customized risk assessment models and include broader customer categories in the loan approval.
Collect relevant data about your applicants with apps, mobile banking products, and card use analytics to understand their spending habits and loan repayment potential. This way, you will get a better idea of their creditworthiness than a usual balanced scorecard gives.
#2 Accurate Analysis
Data acquisition is only the beginning. You need to analyze the collected dataset to make sense of the data bits and form a coherent risk portfolio for every applicant. Make realistic risk categories and assess applicants against a wide set of criteria; it’s possible with advanced digital tools.
#3 Tech-Savvy Risk Assessment
You should stay alert to market changes and make your strategy flexible to embrace new opportunities and address challenges. Intelligent data and cutting-edge analytical tools are your secret weapon in the competitive sector that can win you a stable position.
AI, ML, predictive modeling, and advanced credit scoring are a winning combination in the modern lending business. So, you can apply this mix to improve your operations and reach out to broader populations in the gray area without significant business risks.
Expand your customer base with the right strategy
As you can see, the “gray zone” of the lending market has its benefits and risks. Working with not-so-reliable clients is a more risky business than serving only people with ideal track records.
Still, with modern tech tools combining AI models and Machine Learning algorithms, you can bring the risk down to a minimum and embrace a huge untapped market sector.
Such efforts may be successful with proper arrangement of credit decision-making and smart lending data use, which we deal with in the next chapter.
Part 4. The Complete Guide to Data Management in Lending Institutions
Data is at the heart of an effective digital business today. Proper data management and the ability to make sense of big data give you a critical competitive advantage. Data grows more complex today, but its importance is also increasing, with 90% of C-level staff recognizing its key role in digital transformation.
Let’s take a closer look at the link between data and effective digital transformation, and discuss how the proper use of lending data can boost your transformation efforts and reduce lending risks.
Why does data matter?
According to analysts, 75% of companies want to include data in their digital transformation efforts, while only around 24% succeed in that. As a result, businesses encounter the following problems:
- Poor decision-making. If your company makes decisions based on wrong or incomplete data, many processes will suffer, from target market identification to pricing strategy to CRM.
- Data security risks. Improper data management opens doors for hackers and criminals, thus putting your customers and your business survival under threat.
- Low CX. Modern businesses make decisions in tandem with customers, so remaining deaf to customer feedback is a path to nowhere.
Failed digital transformation. Business processes can’t be effectively transformed based on wrong decisions and tools.
Data management best practices
Here’s how you can prepare for an effective digital transformation by working with your data.
- Data collection. Compile all your data in a single database to simplify its further processing. As a rule, companies neglect this preparatory step, which results in fragmented or scattered data and transformation hurdles.
- Data processing. It’s vital to engage a data engineer (in-house or outsourced) to transform the data into a usable format for further analysis and new software integration.
- Data analysis. Next, it’s time for the data scientist to step in. This can be a team member or an outsourced pro who knows how to make sense of lending data to meet business objectives. Please keep in mind that analysis will be successful only if the data scientist collaborates with product owners and C-level staff, not in isolation.
Such a comprehensive insight into your business data will clarify your current position and highlight future development directions.
The 5 most common data management errors lenders make
Many businesses stepping on the path of digital transformation make identical mistakes. To save you from this trouble, we’ve summarized the most common pitfalls you should avoid by all means.
#1 Data bias
Sometimes data bias is unavoidable. Still, the biggest mistake is to think that you know how your customers and competitors will behave in the future. It always makes sense to test your hypotheses with data and stay alert to market changes.
#2 Poor governance
Your digital transformation may stagnate even if you have a good data model. Besides access to high-quality data, businesses must learn to use it correctly and with maximum value. Thus, you should test your data models regularly to ensure their efficiency.
#3 Lack of cooperation
Corporate silos can kill your transformation’s progress. The IT department may be clueless about the decisions made by C-level executives, and such a divide is counter-productive for any change. Deal with this bottleneck by engaging IT staff in decision-making and ensuring the whole process’s transparency.
#4 Lack of proper staff
You can’t ensure a smooth digital transformation if you have no data science staff. It doesn’t matter whether you hire in-house experts or an outsourced team for this change; the main thing is to have properly trained data scientists and engineers on board.
#5 Limited data use
Sticking to the data from your internal data warehouse may limit your decision-making to past data. A better solution is to engage external data sources and make decisions on real-time data changes. This way, you will capture the momentum and respond to market challenges on time.
Transform your data into a business asset
Just fancy: 2.5 quintillion bytes of new data emerge daily in the digital space. Handling this data properly is the secret to business success in the modern world.
How can your lending business embrace the big data in your sector?
Data identification: two source types
Let’s face it: you won’t be able to process all data. It just doesn’t make sense. So, you need to determine the data you need and then develop workable ways of its efficient extraction.
Data that may be of use for your lending organization comes from two source types:
- Internal sources. You have much customer data at hand; it’s the data they share with you when registering in your apps or submitting loan applications. Thus, you can make decisions based on the user behavior and demographics, their behavior in the apps and on website pages, as well as their financial product choices.
- Alternative data sources. Third-party data providers include credit bureaus, FinTech entities, and social media. You can derive many unstructured data from those sources, or tap the already processed and organized data available on demand.
You can reap insightful data from the following sources:
- Financial activity data (expenditures, payments, revenue sources).
- Social activities.
- Behavioral data.
- Data from aggregators.
- Telecom data.
- Additional sources.
Next, you should develop a data analysis strategy to extract relevant insights from raw data.
Ask yourself these questions when introducing a new data source
When sorting out the available data and choosing third-party data providers, you should ask the following questions:
- What can this new data tell me about customers?
- How can I integrate the new data and existing data?
- Will this data inform my evaluation of credibility and affordability?
- How should I process this data to gain valuable insights from it?
- How quickly can I get this data, and from what channel?
- How often does this data need to be updated?
- Will this data provide positive or negative insights about clients?
- Is the data source legal?
- How much will it cost me?
By incorporating these questions into your data selection strategy, you will set up effective data processes for timely data collection and relevant use.
Must-do steps for choosing a data provider
The more data you use, the costlier your decision-making gets, which is often irrational. It’s recommended that lenders evaluate data before including it in the decision-making pipeline.
Don’t ignore any of these four steps, as each is instrumental for your credit decision optimization.
Historical testing. Ask the provider of your choice to process your selected dataset and evaluate the outcome first. It’s better to test the provider’s data processing approach on historical data before paying them money and engaging in a long-term relationship.
Pilot project. Embarking on a large-scale data processing project requires time and money on both sides. Thus, you may save funds and nerves by entrusting a small pilot project first. This way, you will see how the provider works and will be able to make minor tweaks before the large-scale work starts.
Calculations. Once you test the data provider’s expertise and decide to work with it, it’s time to negotiate the pricing in detail. You surely want to avoid hidden costs and expensive extras, so setting up the pricing structure is a good start to a mutually satisfying collaboration.
Decision-making. The final step is the top management’s decision about choosing a particular data provider. This decision will be informed and consistent if you complete the previous steps and build a clear business case for data provider use, with a breakdown of prices and expected results.
Don’t focus only on negative data: gain perspective
Lending businesses often focus on negative data as the primary factor in risk assessment. Indeed, negative client data may help avoid suspicious clients and reject dubious applications.
Four tips for efficient data management
As big data enters the global lending business, it’s time to learn to manage it effectively, as this competency will ultimately become your competitive advantage.
Pro tips for effective work with data sources:
- Stay alert to updates. Something new emerges in the FinTech market every day. Thus, you must keep your finger on the pulse to integrate the latest tech tools into your digital transformation plan.
- Don’t hurry. It’s vital to approach digital transformation and new data sources implementation as a process, not an outcome, and to carefully plan the budget for that transition.
- Outsource. No need to hire in-house data engineers if you can delegate most of the digital transformation tasks to a dedicated team. Find a reliable partner to free your core staff from this additional workload.
- Prepare data. Data preparation is a vital step for using AI algorithms and models. So, take care of your raw data to organize the processes wisely.
Universal rules for working with credit data
Every lending organization’s unique definition of a good data source depends on its scale, approach, and strategy.
Several rules are universal for all financial market players:
- Engage as many data sources as you can.
- Utilize several scoring models.
- Validate your decisions with numerous methods.
This way, you can refine your decisions based on what works and what doesn’t, effectively reducing business risks without losing essential business opportunities.
Use data as a secret digital transformation weapon
By optimizing the internal data management processes, you’re sure to get a 360-degree view of your business and streamline your growth.
Data is a secret weapon in the modern data-driven business landscape, so taking your data under control is a sure way to reduce risks, improve operations, and embrace new business opportunities.
The use of AI tools may be of much help in this process – we dwell on this technology in more detail next.
Part 5. How Artificial Intelligence Can Boost Your Lending Business
The AI market size in FinTech is anticipated to see a CAGR of 25.3% (2022-2027). More and more financial companies are joining this trend, but many are confused about the best way of integrating AI into their practices.
Credit organizations can use a variety of options for AI use.
Here we discuss the most relevant AI tools hand-picked for the lending business and analyze their pros and cons. You’ll also find out what AI data sources can accelerate and inform your successful digital transformation efforts.
AI models to boost your lending business
You can apply AI insights in your lending organization using the following models:
- Client scoring. This model helps companies evaluate the applicant’s creditworthiness.
- Behavioral scoring. This model evaluates customers’ financial behavior and expenditure patterns to estimate precise credit risk.
- Fraud scoring. It uses big data to predict the likelihood of fraud.
- Pricing elasticity forecast. This model provides in-depth insights into every application’s pricing, repayment, and rate variation.
- Propensity score. This model provides accurate budgeting to engage the most beneficial customers.
- Customer lifetime value. This model calculates the expected time an applicant will stay with your company.
- Churn rate. This model helps estimate the number of clients who abandon your service.
- Computer vision tech. These AI tools help companies comply with KYC and AML requirements by checking the applicants’ documents and IDs.
- Geolocation analysis. The GPS tracking tools help customize service to the customers’ specific locations.
In the chart below, you can see how financial institutions are expanding their use of AI technologies to improve their internal processes and enhance the customer experience.
Strengths and weaknesses of AI models
Introducing AI into your business operations is always a hassle. It’s vital to estimate the pros and cons of using AI in your lending organization before embarking on the costly digital transition.
- AI guarantees more predictive power and calculation accuracy in your lending organization’s functioning. This way, AI tools inform quicker and more precise decisions.
- AI can make sense of different data sources, going far beyond the table data format. Now you can make decisions based on audio, video, photo content, and much more.
- Finally, AI develops quickly, giving you the agility and flexibility needed in the dynamic financial market. No need to rebuild the whole AI system from zero; you can introduce minor tweaks to make your AI-powered decision engine more powerful.
- You should still keep in mind that AI is a relatively new concept that is undergoing many changes today. It’s an advanced technology with low transparency and explainability because the AI algorithms represent a black box. The user community still needs to wait for AI models to become more transparent and manageable.
- Data cleaning is a time-consuming task mandatory for AI-powered data processing. AI models work only with clean data, but once they get such datasets, their predictions are quick and accurate.
- AI models are less stable and more expensive than previously used log-regression models. So, your lending organization should factor in the extensive budget for innovative software, data storage, and data scientists’ recruitment when planning AI integration.
How AI transforms banking for a customer
In the diagram below, see an example of how AI technology is transforming the customer experience. The experience with the financial organization becomes intelligent, personalized, and most importantly, goes beyond banking.
AI data sources to feed your credit decisions
AI systems operate based on big data, so you need to gather that input for your AI algorithms’ good work.
Here are the sources of such data:
- Data providers. There is a list of well-known, standard data providers to derive valuable data. It’s a variety of credit bureaus, AML/KYC lists, and the like. You need to choose data sources depending on your business type and needs so that you get the right input for your decision-making models.
- Internal data. You can base decisions on the historical data of the applicant stored in your database or accessible through your channels (e.g., bank records, a record of expenditures, etc.).
- Alternative sources. The choice of alternative data providers depends on the jurisdiction of your operation. You may reap valuable data from online data aggregators, which is a popular option among FinTech startups.
- Open banking ecosystem. This method has gained momentum since the time of COVID-19 onset. It allows a credit organization to access the applicant’s financial data in another financial organization if the applicant agrees to grant that access.
Artificial Intelligence webinar
Watch our latest webinar on Artificial Intelligence in the lending industry. Here, you’ll find the complete guide on AI models, data sources, automated decisions, and more.
AI is a driver of change
AI is firmly entering the lending business today, as many AI-powered models can improve the companies’ decision-making and allow them to make sense of masses of raw data about clients.
Part 6. Software Platform Lenders Need For Digital Transformation
Software is your practical tool for making digital change happen.
As of 2020, 50% of financial companies have invested in digital transformation as their number one development target, compared to only 30% in 2017-2019. However, not all of them have succeeded because of the wrong software tool choices.
Here, we discuss the tools you need for proper transformation that you can implement with cybersecurity, CX, and regulatory compliance in mind.
Scalable decision-making platform: a clue to success
A 2020 survey by FICO & American Banker revealed that banks with top-tier transformational potential scored low on service personalization, data use, and new strategy development.
These failures become even more pronounced compared to the agility and flexibility of FinTech operations, which improve CX and contribute to effective customer retention.
A workable solution for forward-looking banks is an integrated, scalable platform that unifies an organization’s people, technologies, and processes.
Data becomes a transformation bottleneck in many lending organizations, as they need a secure data storage and tiered access to that data for various departments.
Only 5% of banks use all data they have in business-level decision-making compared to 31% of FinTech entities. This meager percentage of efficient data use thus hinders the lenders’ progress and limits their operational performance.
A viable solution is a loan management platform that would organize an effective business data storage system for transparent data analysis, documentation flows, and quick loan decisions. Cloud storage may eliminate infrastructure costs and ensure that employees, customers, and partners can access only relevant data.
According to a recent Interpol study, the number of cyberattacks at critical infrastructures increased almost six-fold, with an 8x increase in risky digital activities.
These figures suggest that many financial organizations prioritize CX and ignore the cybersecurity dimension of their digital transformation. As a result, the average cost of data leakage has already exceeded $3.86 million, with a regular lender forced to invest an additional $900,000 to repair their IT infrastructure.
Fraud prevention is cheaper than remediation
A lending organization can never go wrong when implementing a cutting-edge loan management platform blending AI, ML, big data, and blockchain technologies.
The benefits of such a solution are:
- Centralized data storage in the cloud with ready access to all authorized stakeholders.
- Electronic data flows and documentation exchanges.
- Real-time detection of fraud and cyberattack attempts.
- Robust two-factor authentication and biometric identification.
- Efficient use of proxy, VPN, and device verification methods.
These features enhance user trust and ensure end-to-end business security.
Compliance is a must for any lending organization, and compliance checks usually take much time and human effort if done manually.
Once an organization integrates a loan management platform, it can automate compliance checks and avoid costly fines, reputation damage, and critical loss of customer data and trust.
Such solutions ensure automated compliance tracking at all stages of the loan’s lifecycle, from a loan contract to debt collection and reporting.
Software and customer experience
CX is critical for the lending organization’s success. Slow and outdated processes, paperwork, and service fragmentation can undermine CX. Thus, an innovative loan management platform can address the existing CX issues with proactive solutions.
- Comprehensive user data collection from various channels and efficient data management.
- Self-service features for borrowers (e.g., chatbots and online help).
- Accurate decision-making based on AI, ML, and RPA insights.
- Efficient omnichannel marketing to Millennials and digital natives.
- Advanced service personalization via GPS and credit history analysis.
- Data use authorization by the client to meet compliance requirements.
Mobile service expansion is also a CX priority, as only 44% of financial organizations allow loan applications from mobile devices, compared to 85% of lenders processing online applications.
A unified Lending Management System is a key
Using the right software tools, you can optimize business processes to achieve better efficiency and a strong customer appeal.
This is all possible with a loan management platform that can automate data management and bring all operational aspects into synergy.
A decision-making solution can also help a business achieve these transformation goals. We discuss this concept in more detail in the final part of our guide.
Part 7. Crucial Things To Consider When Updating Your Decision-Making Solution
AI introduction in financial operations is still low, with only 25% of banks with assets exceeding $100 billion using AI decision-making tools. Compliance concerns, fear of additional costs for staff retraining, and ambiguity about AI use explain the delay in AI adoption.
Still, once understood well, AI can improve any banking process. Here is a case of AI benefits for decision-making in banking.
Here, we talk about decision-making engines that can transform your operations from the ground up.
Components of decision-making engine
The decision to approve or reject a loan application is a complex, multi-stage process in any lending organization. It is meant to reduce risks and increase business profits. As a rule, a lender needs to consider the following factors.
- Eligibility rules. Is the applicant eligible for a loan?
- Anti-fraud rules. Does the application violate anti-fraud regulations?
- Maximum loan amount. What is the maximum sum you can lend to a specific applicant?
- Interest rate. What interest rate matches the client’s profile?
- Level of verification. Should the applicant’s documents be verified manually or automatically?
- Level of underwriting. Whose approval is needed to disburse the loan? The decision depends on the loan’s amount and other applicant-related factors.
- Cross-/Upsell. Will this loan be valuable for your company, or is it better to offer another product to the applicant?
- Pre-collection treatment decisions. How to address the client’s inability to repay the loan?
- Collection and recovery analytics. Does your predictive model suggest that the client will manage to return to the repayment schedule?
The good news is that most of these steps can be automated, with AI systems trained to detect anomalies and filter the applications based on your unique credit strategy.
According to the 2020 Executive Survey by FICO, only half of all financial organizations use their data effectively.
Which features lenders need in a credit decision engine
Your organization will make efficient decisions if its credit decision engine includes the following elements.
- Low-code/no-code rule builder. The builder helps you configure new modules without external help or with little assistance from tech specialists.
- Python scripts container. Python is a user-friendly, accessible programming language for manual code scripting. This way, your team may customize the scripts without resorting to low-code systems.
- ML/AI model hosting. This feature imports scoring cards and algorithms to inform credit-related decisions.
- Integration function. This function allows your platform to link with third-party data sources and interact with external systems.
- What-if analysis and scenario analysis. This feature ensures the system’s flexibility and responsiveness to changing decision-making rules without full system recording.
- Step-by-step debugging. The step-by-step application breakdown allows deeper insight into the process and timely troubleshooting.
- Version history and comparison. History tracking allows larger teams to collaborate more effectively with all change versions at hand.
- Logging and audit. The audit of each employee’s activities in the system ensures higher transparency of the credit process.
- CI/CD & Dependency management. This function allows new “versions” and “releases” to test new credit decision rules without disrupting the operating system.
Innovative lending organizations make more frequent update releases to keep their systems relevant and responsive to changing market conditions.
Bottlenecks of credit policy automation
Digital transformation never goes smoothly. Here are the most common challenges lending companies can face in the process.
- Credit policy vs. decision engine changes. If a lender adjusts its credit logic too often, the process may get too complex or costly to meet the digital transformation targets. Thus, it often makes sense to change the credit policy instead of the decision engine system.
- IT department’s overload. IT staff are responsible for the credit decision engine’s updates, maintenance, and other business tasks. That may be too much for the IT department, so you may outsource credit decision engine updates to a dedicated provider.
- The growing complexity of automation. As you automate the credit decision engine, its decision rules grow more sophisticated. You should consider this change in your automaton strategy. Sometimes automating only 30% of decisions may be much more manageable than 100% automation in one go.
- Changes to rules and modules. Your solutions will grow more complex as you approach Big Data. You should adjust the credit policy and decision-making complexity to these changes.
- Automation vs. manual coding. Python is effective in credit decision systems, but it may not be enough to handle your updated system. It’s vital to balance manual coding with automated tools to reach optimal performance.
The steps to fully centralized decisioning
Implementing a decision engine system and advanced data ownership is a long process. Companies have been moving through it step-by-step for several years.
In the chart below, see different levels of data management. What level is your company at?
Determine what position you are in and plan the necessary actions to move up to the next level.
Move toward making effective decisions
Automated decision-making engines using Big Data and smart analytical algorithms are a rare find for modern lending businesses. They can become your magic wand for a quick and smooth business transformation because of robust automation, tight data control, and centralization of all data-related operations.
There is no universal recipe for an effective digital transformation, as every business is unique. You should first set a roadmap for your transition efforts, depending on where you are now and what you want to achieve with tech innovation.
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